{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"![Screen Shot 2021-06-20 at 11.28.12.png](attachment:68f00087-62d4-4303-a40e-ff5279194a3f.png)\n<center>\n<span style=\"font-family: Arial; font-weight:bold;font-size:1.9em;color:#9900ff\"> <h3> Pneumonia Detection Using computer vision\n</center>\n<div>\n    <span style=\"font-family: Arial; font-weight:bold;color:#ff00ff\">\n\n##### OverView:\n    Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. In the United States, pneumonia accounts for over 500,000 visits to emergency departments [1] and over 50,000 deaths in 2015 [2], keeping the ailment on the list of top 10 causes of death in the country.\n    While common, accurately diagnosing pneumonia is a tall order. It requires review of a chest radiograph (CXR) by highly trained specialists and confirmation through clinical history, vital signs and laboratory exams. Pneumonia usually manifests as an area or areas of increased opacity [3] on CXR. However, the diagnosis of pneumonia on CXR is complicated because of a number of other conditions in the lungs such as fluid overload (pulmonary edema), bleeding, volume loss (atelectasis or collapse), lung cancer, or post-radiation or surgical changes. Outside of the lungs, fluid in the pleural space (pleural effusion) also appears as increased opacity on CXR. When available, comparison of CXRs of the patient taken at different time points and correlation with clinical symptoms and history are helpful in making the diagnosis.\n        CXRs are the most commonly performed diagnostic imaging study. A number of factors such as positioning of the patient and depth of inspiration can alter the appearance of the CXR [4], complicating interpretation further. In addition, clinicians are faced with reading high volumes of images every shift.\n        The RSNA is an international society of radiologists, medical physicists and other medical professionals with more than 54,000 members from 146 countries across the globe. They see the potential for ML to automate initial detection (imaging screening) of potential pneumonia cases in order to prioritize and expedite their review.\n        \n##### Objective:\n    In this capstone project, the goal is to build a pneumonia detection system, to locate the position of inflammation in an image.\n\n##### Exploratory Data Analysis\n    - Univariate Analysis - Outlier and Frequency Analysis\n    - Bivariate Analysis - Visu**alization\n    - Variable Reduction - Multicollinearity\n\n##### Data Pre-Processing - \n    - Missing Values Treatment \n    - The IoU of a set of predicted bounding boxes and ground truth bounding boxes is calculated as:\n        𝐼𝑜𝑈(𝐴,𝐵)=𝐴∩𝐵𝐴∪𝐵.\n    -The average precision of a single image is calculated as the mean of the above precision values at each IoU  threshold =   1|𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑𝑠|∑𝑡𝑇𝑃(𝑡)𝑇𝑃(𝑡)+𝐹𝑃(𝑡)+𝐹𝑁(𝑡).\n    \n##### Model Build and Model Diagnostics\n    - Train and Test split\n    - MobileNet\n    - Resnet50\n    - DenseNet121\n\n##### Model Validation\n       -Dice Coeffiecient\n       -Precision\n       -Accuracy\n    - \n##### Choose the best model\n    -  yet to tune the models\n\n\n</div>","metadata":{},"attachments":{"68f00087-62d4-4303-a40e-ff5279194a3f.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"**Import the Necessary Libraries**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nimport pydicom as dcm\nimport os\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.image import ImageDataGenerator, load_img\nfrom matplotlib.patches import Rectangle\nimport cv2\nfrom tensorflow.keras.applications.mobilenet import preprocess_input\nimport tensorflow\nfrom tensorflow.keras.layers import *\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\nimport keras\nimport gc\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D,GlobalAveragePooling2D, Flatten, Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam","metadata":{"execution":{"iopub.status.busy":"2021-07-08T04:40:46.697392Z","iopub.execute_input":"2021-07-08T04:40:46.697768Z","iopub.status.idle":"2021-07-08T04:40:52.270941Z","shell.execute_reply.started":"2021-07-08T04:40:46.697685Z","shell.execute_reply":"2021-07-08T04:40:52.270078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Load Datasets**","metadata":{}},{"cell_type":"code","source":"label_data = pd.read_csv(\"../input/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv\")","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:51:18.054857Z","iopub.execute_input":"2021-06-20T16:51:18.055264Z","iopub.status.idle":"2021-06-20T16:51:18.115595Z","shell.execute_reply.started":"2021-06-20T16:51:18.055229Z","shell.execute_reply":"2021-06-20T16:51:18.11459Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:38.98797Z","iopub.execute_input":"2021-06-20T14:35:38.988331Z","iopub.status.idle":"2021-06-20T14:35:39.019608Z","shell.execute_reply.started":"2021-06-20T14:35:38.988298Z","shell.execute_reply":"2021-06-20T14:35:39.018481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info = pd.read_csv(\"../input/rsna-pneumonia-detection-challenge/stage_2_detailed_class_info.csv\")","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:51:21.662328Z","iopub.execute_input":"2021-06-20T16:51:21.662699Z","iopub.status.idle":"2021-06-20T16:51:21.714358Z","shell.execute_reply.started":"2021-06-20T16:51:21.662666Z","shell.execute_reply":"2021-06-20T16:51:21.713358Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info.head(4)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.080929Z","iopub.execute_input":"2021-06-20T14:35:39.081315Z","iopub.status.idle":"2021-06-20T14:35:39.096171Z","shell.execute_reply.started":"2021-06-20T14:35:39.081283Z","shell.execute_reply":"2021-06-20T14:35:39.094907Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.merge(left = class_info, right = label_data, how = 'left', on = 'patientId')\ndf = df.drop_duplicates()\ndf.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:54:08.535641Z","iopub.execute_input":"2021-06-20T16:54:08.536114Z","iopub.status.idle":"2021-06-20T16:54:08.600982Z","shell.execute_reply.started":"2021-06-20T16:54:08.536077Z","shell.execute_reply":"2021-06-20T16:54:08.599795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Database table Preprocessing**","metadata":{}},{"cell_type":"code","source":"def check_for_missing_data(df):\n    total = df.isnull().sum().sort_values(ascending=False)   # total number of null values\n    percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False) #percentage of values that are null\n    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent']) # putting the above two together\n    return missing_data # return the dataframe","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.170101Z","iopub.execute_input":"2021-06-20T14:35:39.170602Z","iopub.status.idle":"2021-06-20T14:35:39.181686Z","shell.execute_reply.started":"2021-06-20T14:35:39.17057Z","shell.execute_reply":"2021-06-20T14:35:39.180542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_for_missing_data(label_data)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.186359Z","iopub.execute_input":"2021-06-20T14:35:39.186695Z","iopub.status.idle":"2021-06-20T14:35:39.230939Z","shell.execute_reply.started":"2021-06-20T14:35:39.186663Z","shell.execute_reply":"2021-06-20T14:35:39.229681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_for_missing_data(class_info)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.233546Z","iopub.execute_input":"2021-06-20T14:35:39.233996Z","iopub.status.idle":"2021-06-20T14:35:39.266202Z","shell.execute_reply.started":"2021-06-20T14:35:39.233955Z","shell.execute_reply":"2021-06-20T14:35:39.26479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"No missing value found in the class_info table\n\nAll the target values are filled therefore there is no empty value, cell with Nan or empty cell are having no phenumonia so we should replace those value with 0","metadata":{}},{"cell_type":"code","source":"label_data.fillna(0, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:30:32.820746Z","iopub.execute_input":"2021-06-20T16:30:32.821097Z","iopub.status.idle":"2021-06-20T16:30:32.832229Z","shell.execute_reply.started":"2021-06-20T16:30:32.821063Z","shell.execute_reply":"2021-06-20T16:30:32.831109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_data.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.281353Z","iopub.execute_input":"2021-06-20T14:35:39.282096Z","iopub.status.idle":"2021-06-20T14:35:39.302147Z","shell.execute_reply.started":"2021-06-20T14:35:39.282049Z","shell.execute_reply":"2021-06-20T14:35:39.300834Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"x, y, width, height are float type therefore we can say that they all have the numerical values","metadata":{}},{"cell_type":"code","source":"label_data[label_data[\"Target\"]==1].describe().T","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.303717Z","iopub.execute_input":"2021-06-20T14:35:39.304209Z","iopub.status.idle":"2021-06-20T14:35:39.34874Z","shell.execute_reply.started":"2021-06-20T14:35:39.304162Z","shell.execute_reply":"2021-06-20T14:35:39.34735Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"from here we can get an idea of mean width and mean height of the phenumoniatic image areas","metadata":{}},{"cell_type":"code","source":"np.unique(class_info[\"class\"])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.350722Z","iopub.execute_input":"2021-06-20T14:35:39.351221Z","iopub.status.idle":"2021-06-20T14:35:39.382895Z","shell.execute_reply.started":"2021-06-20T14:35:39.351176Z","shell.execute_reply":"2021-06-20T14:35:39.381216Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can divide the dataset in 3 classes\n\n1. confirmed phenumoia\n2. partial phenumonia\n3. Normal Lungs","metadata":{}},{"cell_type":"code","source":"np.unique(label_data[\"Target\"])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.385183Z","iopub.execute_input":"2021-06-20T14:35:39.386174Z","iopub.status.idle":"2021-06-20T14:35:39.397205Z","shell.execute_reply.started":"2021-06-20T14:35:39.386103Z","shell.execute_reply":"2021-06-20T14:35:39.395746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.countplot(label_data[\"Target\"])\n","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.399186Z","iopub.execute_input":"2021-06-20T14:35:39.40022Z","iopub.status.idle":"2021-06-20T14:35:39.58198Z","shell.execute_reply.started":"2021-06-20T14:35:39.400173Z","shell.execute_reply":"2021-06-20T14:35:39.580846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We may need to upsample 1 so that the model doesn't biase","metadata":{}},{"cell_type":"code","source":"sns.countplot(class_info[\"class\"])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.583599Z","iopub.execute_input":"2021-06-20T14:35:39.584461Z","iopub.status.idle":"2021-06-20T14:35:39.765251Z","shell.execute_reply.started":"2021-06-20T14:35:39.584408Z","shell.execute_reply":"2021-06-20T14:35:39.763702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info[class_info[\"patientId\"]=='0004cfab-14fd-4e49-80ba-63a80b6bddd6']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.767341Z","iopub.execute_input":"2021-06-20T14:35:39.767849Z","iopub.status.idle":"2021-06-20T14:35:39.785628Z","shell.execute_reply.started":"2021-06-20T14:35:39.767801Z","shell.execute_reply":"2021-06-20T14:35:39.784209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_data[label_data['patientId']=='0004cfab-14fd-4e49-80ba-63a80b6bddd6']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.787681Z","iopub.execute_input":"2021-06-20T14:35:39.788482Z","iopub.status.idle":"2021-06-20T14:35:39.810857Z","shell.execute_reply.started":"2021-06-20T14:35:39.788433Z","shell.execute_reply":"2021-06-20T14:35:39.809358Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"No Lung Opacity / Not Normal has been classified as Target 0","metadata":{}},{"cell_type":"code","source":"class_info[class_info[\"patientId\"]=='003d8fa0-6bf1-40ed-b54c-ac657f8495c5']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.813049Z","iopub.execute_input":"2021-06-20T14:35:39.813705Z","iopub.status.idle":"2021-06-20T14:35:39.831997Z","shell.execute_reply.started":"2021-06-20T14:35:39.813596Z","shell.execute_reply":"2021-06-20T14:35:39.83047Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_data[label_data['patientId']=='003d8fa0-6bf1-40ed-b54c-ac657f8495c5']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.834202Z","iopub.execute_input":"2021-06-20T14:35:39.834928Z","iopub.status.idle":"2021-06-20T14:35:39.858677Z","shell.execute_reply.started":"2021-06-20T14:35:39.834738Z","shell.execute_reply":"2021-06-20T14:35:39.857437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Noraml has been classified as 0 too","metadata":{}},{"cell_type":"code","source":"class_info[class_info[\"patientId\"]=='00436515-870c-4b36-a041-de91049b9ab4']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.861996Z","iopub.execute_input":"2021-06-20T14:35:39.862842Z","iopub.status.idle":"2021-06-20T14:35:39.880005Z","shell.execute_reply.started":"2021-06-20T14:35:39.862794Z","shell.execute_reply":"2021-06-20T14:35:39.878887Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_data[label_data['patientId']=='00436515-870c-4b36-a041-de91049b9ab4']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.882667Z","iopub.execute_input":"2021-06-20T14:35:39.883045Z","iopub.status.idle":"2021-06-20T14:35:39.905774Z","shell.execute_reply.started":"2021-06-20T14:35:39.882967Z","shell.execute_reply":"2021-06-20T14:35:39.904093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Lung Opacity has been classified as 1 and other classes has been classified as Normal indicated as 0\n\nThe class value 'No Lung Opacity / Not Normal ' is classified as 0 as it could be case of other complication which is not phenumonia","metadata":{}},{"cell_type":"code","source":"pd.pivot_table(df,index=[\"class\"], values=['patientId'], aggfunc='count')","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:35:39.914222Z","iopub.execute_input":"2021-06-20T14:35:39.914561Z","iopub.status.idle":"2021-06-20T14:35:39.940422Z","shell.execute_reply.started":"2021-06-20T14:35:39.914531Z","shell.execute_reply":"2021-06-20T14:35:39.939214Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"column_list = [\"Patient ID\", \"Patient Sex\", \"Patient's Age\", \"View Position\", \"Image Size\"]\nfile_meta_Data = pd.DataFrame(columns=column_list)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:31:25.722042Z","iopub.execute_input":"2021-06-20T16:31:25.722423Z","iopub.status.idle":"2021-06-20T16:31:25.729887Z","shell.execute_reply.started":"2021-06-20T16:31:25.722392Z","shell.execute_reply":"2021-06-20T16:31:25.729011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def add_meta_data_to_df(df, loc, from_list):\n    data = []\n    for filename in from_list:\n            imagePath = loc+filename\n            data_row_img_data = dcm.read_file(imagePath)\n            values = []\n            values.append(data_row_img_data.PatientID)\n            values.append(data_row_img_data.PatientSex)\n            values.append(data_row_img_data.PatientAge)\n            values.append(data_row_img_data.ViewPosition)\n            values.append(f\"{data_row_img_data.Rows}x{data_row_img_data.Columns}\")\n            zipped_val = dict(zip(column_list, values))\n            df = df.append(zipped_val, True)\n    return df","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:51:49.525062Z","iopub.execute_input":"2021-06-20T16:51:49.525436Z","iopub.status.idle":"2021-06-20T16:51:49.532268Z","shell.execute_reply.started":"2021-06-20T16:51:49.525404Z","shell.execute_reply":"2021-06-20T16:51:49.531128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Images Example\ntrain_images_dir = '../input/rsna-pneumonia-detection-challenge/stage_2_train_images/'\ntrain_images = [f for f in os.listdir(train_images_dir) if os.path.isfile(os.path.join(train_images_dir, f))]\ntest_images_dir = '../input/rsna-pneumonia-detection-challenge/stage_2_train_images/'\ntest_images = [f for f in os.listdir(test_images_dir) if os.path.isfile(os.path.join(test_images_dir, f))]\nprint('5 Training images', train_images[:5]) # Print the first 5","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:51:52.682837Z","iopub.execute_input":"2021-06-20T16:51:52.683301Z","iopub.status.idle":"2021-06-20T16:52:16.883871Z","shell.execute_reply.started":"2021-06-20T16:51:52.683264Z","shell.execute_reply":"2021-06-20T16:52:16.882903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"file_meta_Data = add_meta_data_to_df(file_meta_Data, \"../input/rsna-pneumonia-detection-challenge/stage_2_train_images/\", train_images)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:16.885538Z","iopub.execute_input":"2021-06-20T16:52:16.886176Z","iopub.status.idle":"2021-06-20T16:52:17.158661Z","shell.execute_reply.started":"2021-06-20T16:52:16.886127Z","shell.execute_reply":"2021-06-20T16:52:17.157098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"file_meta_Data.info()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:17.159733Z","iopub.status.idle":"2021-06-20T16:52:17.160171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df[\"class\"].value_counts().plot(kind='pie',autopct='%1.0f%%', shadow=True, subplots=False)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:17.161196Z","iopub.status.idle":"2021-06-20T16:52:17.161937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.countplot(file_meta_Data['View Position'])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:17.163137Z","iopub.status.idle":"2021-06-20T16:52:17.163768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We have 2 view positions in the images","metadata":{}},{"cell_type":"code","source":"sns.distplot(file_meta_Data[\"Patient's Age\"])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:42:16.979702Z","iopub.execute_input":"2021-06-20T14:42:16.980248Z","iopub.status.idle":"2021-06-20T14:42:17.514233Z","shell.execute_reply.started":"2021-06-20T14:42:16.980175Z","shell.execute_reply":"2021-06-20T14:42:17.51317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Number of train images:', len(train_images))\nprint('Number of test images:', len(test_images))","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:42:25.23944Z","iopub.execute_input":"2021-06-20T14:42:25.239922Z","iopub.status.idle":"2021-06-20T14:42:25.251207Z","shell.execute_reply.started":"2021-06-20T14:42:25.239886Z","shell.execute_reply":"2021-06-20T14:42:25.249564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we have to count of Train and Test Data here","metadata":{}},{"cell_type":"code","source":"dicom_file_path = os.path.join(\"../input/rsna-pneumonia-detection-challenge/stage_2_train_images/05eebe4c-bca2-40d4-bb20-54fc60e2bcea.dcm\")\nfile = dcm.read_file(dicom_file_path)\nfile","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:42:29.848695Z","iopub.execute_input":"2021-06-20T14:42:29.8491Z","iopub.status.idle":"2021-06-20T14:42:29.863795Z","shell.execute_reply.started":"2021-06-20T14:42:29.849066Z","shell.execute_reply":"2021-06-20T14:42:29.862308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The above gives more information on the individual data, but in our modeling case these are not relevant","metadata":{}},{"cell_type":"markdown","source":"**Data Visualisation**","metadata":{}},{"cell_type":"code","source":"gc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:45:55.80066Z","iopub.execute_input":"2021-06-20T14:45:55.801087Z","iopub.status.idle":"2021-06-20T14:45:56.050713Z","shell.execute_reply.started":"2021-06-20T14:45:55.801052Z","shell.execute_reply":"2021-06-20T14:45:56.049448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def show_dicom_images_with_boxes(data):\n    img_data = list(data.T.to_dict().values())\n    f, ax = plt.subplots(3,3, figsize=(16,18))\n    for i,data_row in enumerate(img_data):\n        patientImage = data_row['patientId']+'.dcm'\n        imagePath = f\"../input/rsna-pneumonia-detection-challenge/stage_2_train_images/{patientImage}\"\n        data_row_img_data = dcm.read_file(imagePath)\n        modality = data_row_img_data.Modality\n        age = data_row_img_data.PatientAge\n        sex = data_row_img_data.PatientSex\n        data_row_img = dcm.dcmread(imagePath)\n        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) \n        ax[i//3, i%3].axis('off')\n        ax[i//3, i%3].set_title('ID: {}\\nModality: {} Age: {} Sex: {} Target: {}'.format(\n                data_row['patientId'],modality, age, sex, data_row['Target']))\n        rows = label_data[label_data['patientId']==data_row['patientId']]\n        box_data = list(rows.T.to_dict().values())\n        for j, row in enumerate(box_data):\n            ax[i//3, i%3].add_patch(Rectangle(xy=(row['x'], row['y']),\n                        width=row['width'],height=row['height'], \n                        color=\"yellow\",alpha = 0.1))   \n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:53:03.810511Z","iopub.execute_input":"2021-06-20T16:53:03.81086Z","iopub.status.idle":"2021-06-20T16:53:03.822422Z","shell.execute_reply.started":"2021-06-20T16:53:03.810828Z","shell.execute_reply":"2021-06-20T16:53:03.821373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def show_images(data):\n    img_data = list(data.T.to_dict().values())\n    f, ax = plt.subplots(3,3, figsize=(16,18))\n    for i,data_row in enumerate(img_data):\n        patientImage = data_row['patientId']+'.dcm'\n        imagePath = f\"../input/rsna-pneumonia-detection-challenge/stage_2_train_images/{patientImage}\"\n        data_row_img_data = dcm.read_file(imagePath)\n        modality = data_row_img_data.Modality\n        age = data_row_img_data.PatientAge\n        sex = data_row_img_data.PatientSex\n        data_row_img = dcm.dcmread(imagePath)\n        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) \n        ax[i//3, i%3].axis('off')\n        ax[i//3, i%3].set_title('ID: {}\\nModality: {} Age: {} Sex: {} Target: {}\\nWindow: {}:{}:{}:{}'.format(\n                data_row['patientId'],\n                modality, age, sex, data_row['Target'], \n                data_row['x'],data_row['y'],data_row['width'],data_row['height']))\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:30.114126Z","iopub.execute_input":"2021-06-20T14:43:30.114551Z","iopub.status.idle":"2021-06-20T14:43:30.124067Z","shell.execute_reply.started":"2021-06-20T14:43:30.114518Z","shell.execute_reply":"2021-06-20T14:43:30.122659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let display some images that has been classified as a case of phenumonia","metadata":{}},{"cell_type":"code","source":"show_images(label_data[label_data['Target']==1].sample(9))","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:33.46506Z","iopub.execute_input":"2021-06-20T14:43:33.465495Z","iopub.status.idle":"2021-06-20T14:43:35.314234Z","shell.execute_reply.started":"2021-06-20T14:43:33.465461Z","shell.execute_reply":"2021-06-20T14:43:35.312099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Also display the image of normal lungs so that we can have idea about how normal lungs will look","metadata":{}},{"cell_type":"code","source":"show_images(label_data[label_data['Target']==0].sample(9))","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:41.721291Z","iopub.execute_input":"2021-06-20T14:43:41.721678Z","iopub.status.idle":"2021-06-20T14:43:43.523383Z","shell.execute_reply.started":"2021-06-20T14:43:41.721643Z","shell.execute_reply":"2021-06-20T14:43:43.521816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_dicom_images_with_boxes(label_data[label_data['Target']==1].sample(9))","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:48.391974Z","iopub.execute_input":"2021-06-20T14:43:48.392513Z","iopub.status.idle":"2021-06-20T14:43:50.262941Z","shell.execute_reply.started":"2021-06-20T14:43:48.392466Z","shell.execute_reply":"2021-06-20T14:43:50.261561Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info[class_info['patientId']=='00436515-870c-4b36-a041-de91049b9ab4']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:55.869883Z","iopub.execute_input":"2021-06-20T14:43:55.87031Z","iopub.status.idle":"2021-06-20T14:43:55.888303Z","shell.execute_reply.started":"2021-06-20T14:43:55.870275Z","shell.execute_reply":"2021-06-20T14:43:55.886296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info.drop_duplicates(subset='patientId', keep='last', inplace=True)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:43:58.472007Z","iopub.execute_input":"2021-06-20T14:43:58.472456Z","iopub.status.idle":"2021-06-20T14:43:58.492269Z","shell.execute_reply.started":"2021-06-20T14:43:58.472423Z","shell.execute_reply":"2021-06-20T14:43:58.491184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:44:00.930203Z","iopub.execute_input":"2021-06-20T14:44:00.930583Z","iopub.status.idle":"2021-06-20T14:44:00.94022Z","shell.execute_reply.started":"2021-06-20T14:44:00.93055Z","shell.execute_reply":"2021-06-20T14:44:00.938714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = 1024#sample_pixel_array.shape[0]\nADJUSTED_IMAGE_SIZE=224\nMASK_IMAGE_SIZE = 28\nFACTOR = MASK_IMAGE_SIZE/IMAGE_SIZE","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:34:48.574466Z","iopub.execute_input":"2021-06-20T16:34:48.574805Z","iopub.status.idle":"2021-06-20T16:34:48.579031Z","shell.execute_reply.started":"2021-06-20T16:34:48.574774Z","shell.execute_reply":"2021-06-20T16:34:48.577988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_info = shuffle(class_info)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:32:21.368307Z","iopub.execute_input":"2021-06-20T16:32:21.368635Z","iopub.status.idle":"2021-06-20T16:32:21.382769Z","shell.execute_reply.started":"2021-06-20T16:32:21.368606Z","shell.execute_reply":"2021-06-20T16:32:21.381708Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_train, class_val = train_test_split(class_info, test_size=0.10, random_state=42, stratify=class_info['class'])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:44.169571Z","iopub.execute_input":"2021-06-20T16:52:44.169922Z","iopub.status.idle":"2021-06-20T16:52:44.226792Z","shell.execute_reply.started":"2021-06-20T16:52:44.16989Z","shell.execute_reply":"2021-06-20T16:52:44.225954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_feature_tr = []\ny_feature_target_tr = []\ny_feature_coordinates_tr = []\nfrom PIL import Image","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:34:44.044769Z","iopub.execute_input":"2021-06-20T16:34:44.045137Z","iopub.status.idle":"2021-06-20T16:34:44.050511Z","shell.execute_reply.started":"2021-06-20T16:34:44.045103Z","shell.execute_reply":"2021-06-20T16:34:44.049491Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now let's create the mask images are per the given coordinates","metadata":{}},{"cell_type":"code","source":"def create_mask(datafm):\n    X = []\n    y=[]\n    masks = np.zeros((int(datafm.shape[0]), MASK_IMAGE_SIZE, MASK_IMAGE_SIZE))\n    for index, patient_id in enumerate(datafm['patientId'].T.to_dict().values()):\n        image_path = train_images_dir+patient_id+\".dcm\"\n        img = dcm.read_file(image_path)\n        img = img.pixel_array\n        img = cv2.resize(img, (ADJUSTED_IMAGE_SIZE, ADJUSTED_IMAGE_SIZE), interpolation=cv2.INTER_NEAREST)\n        img = Image.fromarray(img)\n        img = img.convert('RGB')\n        img = preprocess_input(np.array(img, dtype=np.float32))\n        X.append(img)\n        rows = label_data[label_data['patientId']==patient_id]\n        y.append(rows['Target'].values[0])\n\n        row_data = list(rows.T.to_dict().values())\n        for row in row_data:\n            x1 = int(row['x']*FACTOR)\n            x2 = int((row['x']*FACTOR)+(row['width']*FACTOR))\n            y1 = int(row['y']*FACTOR)\n            y2 = int((row['y']*FACTOR)+(row['height']*FACTOR))\n            masks[index][y1:y2, x1:x2] = 1\n    del img,row,row_data\n    gc.collect()\n    X=np.array(X)\n    y=np.array(y)\n    return X, y, masks","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:38:01.727918Z","iopub.execute_input":"2021-06-20T16:38:01.728284Z","iopub.status.idle":"2021-06-20T16:38:01.738588Z","shell.execute_reply.started":"2021-06-20T16:38:01.728254Z","shell.execute_reply":"2021-06-20T16:38:01.737667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, y_tr_target, y_train = create_mask(class_train)\nX_val, y_val_target, y_val = create_mask(class_val)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:22.401101Z","iopub.execute_input":"2021-06-20T16:52:22.401467Z","iopub.status.idle":"2021-06-20T16:52:22.405311Z","shell.execute_reply.started":"2021-06-20T16:52:22.401433Z","shell.execute_reply":"2021-06-20T16:52:22.404109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(y_train[18])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:37:58.477048Z","iopub.status.idle":"2021-06-20T14:37:58.478021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 1.50.18.png](attachment:e9371552-32d1-4ae0-8e62-e019cb92a09d.png)","metadata":{},"attachments":{"e9371552-32d1-4ae0-8e62-e019cb92a09d.png":{"image/png":"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"}}},{"cell_type":"code","source":"pidtemp = class_train.iloc[[18]]['patientId']","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:52:48.172488Z","iopub.execute_input":"2021-06-20T16:52:48.172835Z","iopub.status.idle":"2021-06-20T16:52:48.177817Z","shell.execute_reply.started":"2021-06-20T16:52:48.172803Z","shell.execute_reply":"2021-06-20T16:52:48.176749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#show_dicom_images_with_boxes(label_data[label_data['patientId']==pidtemp])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:53:43.64983Z","iopub.execute_input":"2021-06-20T16:53:43.650322Z","iopub.status.idle":"2021-06-20T16:53:43.65525Z","shell.execute_reply.started":"2021-06-20T16:53:43.65028Z","shell.execute_reply":"2021-06-20T16:53:43.654296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\ndef process_dicom_data(data_df):\n    for n, pid in tqdm(enumerate(data_df['patientId'].unique())):\n        # the patientId can have multiple bounding boxes \n        # so there could be different row for each of the bounding box. we jus take the unique patient ids       \n        dcm_file = '../input/rsna-pneumonia-detection-challenge/stage_2_train_images/%s.dcm' % pid #each image file provided are named with patient id\n        dcm_data = dcm.read_file(dcm_file)   #read the file using pydicom\n        idx = (data_df['patientId']==dcm_data.PatientID)\n        data_df.loc[idx,'Modality'] = dcm_data.Modality\n        data_df.loc[idx,'PatientAge'] = pd.to_numeric(dcm_data.PatientAge)\n        data_df.loc[idx,'PatientSex'] = dcm_data.PatientSex\n        data_df.loc[idx,'BodyPartExamined'] = dcm_data.BodyPartExamined\n        data_df.loc[idx,'ViewPosition'] = dcm_data.ViewPosition\n        \n    return data_df","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:53:46.594914Z","iopub.execute_input":"2021-06-20T16:53:46.595353Z","iopub.status.idle":"2021-06-20T16:53:46.603015Z","shell.execute_reply.started":"2021-06-20T16:53:46.595315Z","shell.execute_reply":"2021-06-20T16:53:46.601742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_df = process_dicom_data(df)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:54:17.388711Z","iopub.execute_input":"2021-06-20T16:54:17.389082Z","iopub.status.idle":"2021-06-20T17:00:45.335077Z","shell.execute_reply.started":"2021-06-20T16:54:17.389044Z","shell.execute_reply":"2021-06-20T17:00:45.334166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_df = dicom_df.astype({\"PatientAge\": int})\ndicom_df.fillna(0.0, inplace=True)\ndicom_df.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:45.336758Z","iopub.execute_input":"2021-06-20T17:00:45.337141Z","iopub.status.idle":"2021-06-20T17:00:45.389806Z","shell.execute_reply.started":"2021-06-20T17:00:45.3371Z","shell.execute_reply":"2021-06-20T17:00:45.388511Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dicom_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:45.391822Z","iopub.execute_input":"2021-06-20T17:00:45.392271Z","iopub.status.idle":"2021-06-20T17:00:45.399795Z","shell.execute_reply.started":"2021-06-20T17:00:45.392228Z","shell.execute_reply":"2021-06-20T17:00:45.398681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (30, 10))\nsns.countplot(x = 'PatientAge', hue = 'Target', data = dicom_df)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:45.402009Z","iopub.execute_input":"2021-06-20T17:00:45.402855Z","iopub.status.idle":"2021-06-20T17:00:46.981576Z","shell.execute_reply.started":"2021-06-20T17:00:45.402746Z","shell.execute_reply":"2021-06-20T17:00:46.980659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.countplot(x = 'PatientSex', hue = 'Target', data = dicom_df)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:46.983024Z","iopub.execute_input":"2021-06-20T17:00:46.983394Z","iopub.status.idle":"2021-06-20T17:00:47.153778Z","shell.execute_reply.started":"2021-06-20T17:00:46.983354Z","shell.execute_reply":"2021-06-20T17:00:47.15294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Model Creation**","metadata":{}},{"cell_type":"markdown","source":"**Model I : U-Net**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications.mobilenet import MobileNet\nfrom tensorflow.keras.layers import Concatenate, Conv2D, Reshape, UpSampling2D\nfrom tensorflow.keras.models import Model, load_model","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:47.155141Z","iopub.execute_input":"2021-06-20T17:00:47.155547Z","iopub.status.idle":"2021-06-20T17:00:47.160024Z","shell.execute_reply.started":"2021-06-20T17:00:47.155505Z","shell.execute_reply":"2021-06-20T17:00:47.159154Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ALPHA = 1","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:47.161445Z","iopub.execute_input":"2021-06-20T17:00:47.162097Z","iopub.status.idle":"2021-06-20T17:00:47.170762Z","shell.execute_reply.started":"2021-06-20T17:00:47.162053Z","shell.execute_reply":"2021-06-20T17:00:47.169806Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#this function will creat U-net model\ndef create_model(trainable=True):\n    model = MobileNet(input_shape=(224, 224, 3), include_top=False, alpha=ALPHA, weights=\"imagenet\")\n\n    for layer in model.layers:\n        layer.trainable = trainable\n\n    block1 = model.get_layer(\"conv_pw_5_relu\").output\n    block2 = model.get_layer(\"conv_pw_11_relu\").output\n    block3 = model.get_layer(\"conv_pw_13_relu\").output\n\n    x = Concatenate()([UpSampling2D()(block3), block2])\n    x = Concatenate()([UpSampling2D()(x), block1])\n\n    x = Conv2D(1, kernel_size=1, activation=\"sigmoid\")(x)\n    x = Reshape((28, 28))(x)\n\n    return Model(inputs=model.input, outputs=x)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:47.17318Z","iopub.execute_input":"2021-06-20T17:00:47.173578Z","iopub.status.idle":"2021-06-20T17:00:47.182588Z","shell.execute_reply.started":"2021-06-20T17:00:47.173536Z","shell.execute_reply":"2021-06-20T17:00:47.181264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = create_model(False)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:47.184487Z","iopub.execute_input":"2021-06-20T17:00:47.185064Z","iopub.status.idle":"2021-06-20T17:00:50.180664Z","shell.execute_reply.started":"2021-06-20T17:00:47.184984Z","shell.execute_reply":"2021-06-20T17:00:50.179779Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dice_coefficient(y_true, y_pred):\n    numerator = 2 * tensorflow.reduce_sum(y_true * y_pred)\n    denominator = tensorflow.reduce_sum(y_true + y_pred)\n\n    return numerator / (denominator + tensorflow.keras.backend.epsilon())","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:50.182094Z","iopub.execute_input":"2021-06-20T17:00:50.182464Z","iopub.status.idle":"2021-06-20T17:00:50.190519Z","shell.execute_reply.started":"2021-06-20T17:00:50.182425Z","shell.execute_reply":"2021-06-20T17:00:50.18958Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def loss(y_true, y_pred):\n    return binary_crossentropy(y_true, y_pred) - tensorflow.keras.backend.log(dice_coefficient(y_true, y_pred) + tensorflow.keras.backend.epsilon())","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:50.194057Z","iopub.execute_input":"2021-06-20T17:00:50.194496Z","iopub.status.idle":"2021-06-20T17:00:50.20013Z","shell.execute_reply.started":"2021-06-20T17:00:50.194459Z","shell.execute_reply":"2021-06-20T17:00:50.198858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:01:32.885383Z","iopub.execute_input":"2021-06-20T17:01:32.885729Z","iopub.status.idle":"2021-06-20T17:01:32.890092Z","shell.execute_reply.started":"2021-06-20T17:01:32.885697Z","shell.execute_reply":"2021-06-20T17:01:32.888773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 1.56.18.png](attachment:1fe6021a-79b5-4673-a389-38431b446aec.png)","metadata":{},"attachments":{"1fe6021a-79b5-4673-a389-38431b446aec.png":{"image/png":"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"}}},{"cell_type":"code","source":"y_train.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:01:27.357539Z","iopub.execute_input":"2021-06-20T17:01:27.357931Z","iopub.status.idle":"2021-06-20T17:01:27.361766Z","shell.execute_reply.started":"2021-06-20T17:01:27.357891Z","shell.execute_reply":"2021-06-20T17:01:27.36063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 1.57.22.png](attachment:0efae77a-0ae6-4d30-b1a1-9807a6d32b9a.png)","metadata":{},"attachments":{"0efae77a-0ae6-4d30-b1a1-9807a6d32b9a.png":{"image/png":"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"}}},{"cell_type":"code","source":"X_val.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:01:22.094564Z","iopub.execute_input":"2021-06-20T17:01:22.094935Z","iopub.status.idle":"2021-06-20T17:01:22.098654Z","shell.execute_reply.started":"2021-06-20T17:01:22.094902Z","shell.execute_reply":"2021-06-20T17:01:22.097569Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 1.58.20.png](attachment:43e8e679-0131-46c6-aa55-bd4328371e57.png)","metadata":{},"attachments":{"43e8e679-0131-46c6-aa55-bd4328371e57.png":{"image/png":"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"}}},{"cell_type":"code","source":"y_val.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:01:16.111182Z","iopub.execute_input":"2021-06-20T17:01:16.1116Z","iopub.status.idle":"2021-06-20T17:01:16.115497Z","shell.execute_reply.started":"2021-06-20T17:01:16.111561Z","shell.execute_reply":"2021-06-20T17:01:16.114463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 1.59.19.png](attachment:1e4fe86e-55bb-4fb2-8592-dbe5c179fc45.png)","metadata":{},"attachments":{"1e4fe86e-55bb-4fb2-8592-dbe5c179fc45.png":{"image/png":"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"}}},{"cell_type":"code","source":"from tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.losses import binary_crossentropy\n\noptimizer = Adam(lr=1e-2, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\nmodel.compile(loss=loss, optimizer=optimizer, metrics=[dice_coefficient])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:50.201643Z","iopub.execute_input":"2021-06-20T17:00:50.202025Z","iopub.status.idle":"2021-06-20T17:00:50.222839Z","shell.execute_reply.started":"2021-06-20T17:00:50.201973Z","shell.execute_reply":"2021-06-20T17:00:50.221847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\ncheckpoint = ModelCheckpoint(\"model-{val_loss:.2f}.h5\", monitor=\"val_loss\", verbose=1, save_best_only=True, save_weights_only=True)\n\nstop = EarlyStopping(monitor=\"val_loss\", patience=2)\n\nreduce_lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.2, patience=2, min_lr=1e-6, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:00:50.225053Z","iopub.execute_input":"2021-06-20T17:00:50.225706Z","iopub.status.idle":"2021-06-20T17:00:50.233173Z","shell.execute_reply.started":"2021-06-20T17:00:50.22566Z","shell.execute_reply":"2021-06-20T17:00:50.232325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X_train, y_train, validation_data = (X_val, y_val), epochs=8, batch_size=12, verbose=1, callbacks=[checkpoint, stop, reduce_lr])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:01:07.149263Z","iopub.execute_input":"2021-06-20T17:01:07.14965Z","iopub.status.idle":"2021-06-20T17:01:07.153931Z","shell.execute_reply.started":"2021-06-20T17:01:07.149614Z","shell.execute_reply":"2021-06-20T17:01:07.152779Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2021-06-21 at 2.00.50.png](attachment:1a1ba4d7-3e69-4e4c-839a-202d28884e35.png)","metadata":{},"attachments":{"1a1ba4d7-3e69-4e4c-839a-202d28884e35.png":{"image/png":"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"}}},{"cell_type":"code","source":"mask_predicated = model.predict(X_val)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T17:04:31.043957Z","iopub.execute_input":"2021-06-20T17:04:31.044381Z","iopub.status.idle":"2021-06-20T17:04:31.050251Z","shell.execute_reply.started":"2021-06-20T17:04:31.044345Z","shell.execute_reply":"2021-06-20T17:04:31.049165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Model II: Faster RCNN**","metadata":{}},{"cell_type":"code","source":"%%writefile model.py\n\"\"\"\nPython script to prepare FasterRCNN model.\n\"\"\"\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import  FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\ndef model():\n    # load the COCO pre-trained model\n    # we will keep the image size to 1024 pixels instead of the original 800,\n    # this will ensure better training and testing results, although it may...\n    # ... increase the training time (a tarde-off)\n    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, \n                                                                 min_size=1024)\n    # one class is pneumonia, and the other is background\n    num_classes = 2\n    # get the input features for the classifier\n    in_features = model.roi_heads.box_predictor.cls_score.in_features\n    # replace pre-trained head with our features head\n    # the head layer will classify the images based on our data input features\n    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-07-14T16:15:01.033662Z","iopub.execute_input":"2021-07-14T16:15:01.034086Z","iopub.status.idle":"2021-07-14T16:15:01.044696Z","shell.execute_reply.started":"2021-07-14T16:15:01.033980Z","shell.execute_reply":"2021-07-14T16:15:01.043738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile dataset.py\n\n\"\"\"\nPython script to prepare the dataset\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport re\nimport torch\n\nfrom torch.utils.data import Dataset\n\nclass RSNADataset(Dataset):\n    def __init__(self, dataframe, image_dir, transforms=None):\n        super().__init__()\n\n        self.image_ids = dataframe['patientId'].unique()\n        self.df = dataframe\n        self.image_dir = image_dir\n        self.transforms = transforms\n        \n    def __getitem__(self, index: int):\n\n        image_id = self.image_ids[index]\n        records = self.df[self.df['patientId'] == image_id]\n\n        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n\n        boxes = records[['x', 'y', 'width', 'height']].values\n        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n        \n        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n        area = torch.as_tensor(area, dtype=torch.float32)\n\n        # there is only one class\n        labels = torch.ones((records.shape[0],), dtype=torch.int64)\n        \n        # suppose all instances are not crowd\n        iscrowd = torch.zeros((records.shape[0],), dtype=torch.int64)\n        \n        target = {}\n        target['boxes'] = boxes\n        target['labels'] = labels\n        # target['masks'] = None\n        target['patientId'] = torch.tensor([index])\n        target['area'] = area\n        target['iscrowd'] = iscrowd\n\n        if self.transforms:\n            sample = {\n                'image': image,\n                'bboxes': target['boxes'],\n                'labels': labels\n            }\n            sample = self.transforms(**sample)\n            image = sample['image']\n            \n            target['boxes'] = torch.stack(tuple(map(torch.FloatTensor, zip(*sample['bboxes'])))).permute(1, 0)\n\n        return image, target, image_id\n\n    def __len__(self) -> int:\n        return self.image_ids.shape[0]","metadata":{"execution":{"iopub.status.busy":"2021-07-14T16:15:02.141552Z","iopub.execute_input":"2021-07-14T16:15:02.141877Z","iopub.status.idle":"2021-07-14T16:15:02.147685Z","shell.execute_reply.started":"2021-07-14T16:15:02.141849Z","shell.execute_reply":"2021-07-14T16:15:02.146585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile engine.py\n\nimport pandas as pd\nimport dataset\nimport albumentations as A\nimport time\nimport torch\n\nimport numpy as np\nfrom sklearn import metrics\nfrom torch.utils.data import DataLoader\nfrom albumentations.pytorch.transforms import ToTensorV2\nfrom tqdm import tqdm\nfrom albumentations import (\n    HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,\n    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,\n    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,\n    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose\n)\n\n\"\"\"\nComplete mAP code here => https://gist.github.com/tarlen5/008809c3decf19313de216b9208f3734\n\"\"\"\n\ndef calculate_image_precision(gts, preds, thresholds = (0.5, ), form = 'coco') -> float:\n    # https://www.kaggle.com/sadmanaraf/wheat-detection-using-faster-rcnn-train\n    \"\"\"Calculates image precision.\n\n    Args:\n        gts: (List[List[Union[int, float]]]) Coordinates of the available ground-truth boxes\n        preds: (List[List[Union[int, float]]]) Coordinates of the predicted boxes,\n               sorted by confidence value (descending)\n        thresholds: (float) Different thresholds\n        form: (str) Format of the coordinates\n\n    Return:\n        (float) Precision\n    \"\"\"\n    n_threshold = len(thresholds)\n    image_precision = 0.0\n    \n    ious = np.ones((len(gts), len(preds))) * -1\n    # ious = None\n    TP,FP,FN,TN = 0,0,0,0\n\n    for threshold in thresholds:\n        precision_at_threshold,tp ,fp ,fn ,tn  = calculate_precision(gts.copy(), preds, threshold=threshold,\n                                                     form=form, ious=ious)\n        image_precision += precision_at_threshold / n_threshold\n        TP += tp\n        FP += fp\n        FN += fn\n        TN += tn\n    accuracy = (TP + FP)/(TP+FP+FN+TN)\n    return image_precision,TP/4, FP/4, FN/4, TN/4 , accuracy\n\n\ndef calculate_iou(gt, pr, form='pascal_voc') -> float:\n    # https://www.kaggle.com/sadmanaraf/wheat-detection-using-faster-rcnn-train\n    \"\"\"Calculates the Intersection over Union.\n\n    Args:\n        gt: (np.ndarray[Union[int, float]]) coordinates of the ground-truth box\n        pr: (np.ndarray[Union[int, float]]) coordinates of the prdected box\n        form: (str) gt/pred coordinates format\n            - pascal_voc: [xmin, ymin, xmax, ymax]\n            - coco: [xmin, ymin, w, h]\n    Returns:\n        (float) Intersection over union (0.0 <= iou <= 1.0)\n    \"\"\"\n    if form == 'coco':\n        gt = gt.copy()\n        pr = pr.copy()\n\n        gt[2] = gt[0] + gt[2]\n        gt[3] = gt[1] + gt[3]\n        pr[2] = pr[0] + pr[2]\n        pr[3] = pr[1] + pr[3]\n\n    # Calculate overlap area\n    dx = min(gt[2], pr[2]) - max(gt[0], pr[0]) + 1\n    \n    if dx < 0:\n        return 0.0\n    dy = min(gt[3], pr[3]) - max(gt[1], pr[1]) + 1\n\n    if dy < 0:\n        return 0.0\n\n    overlap_area = dx * dy\n\n    # Calculate union area\n    union_area = (\n            (gt[2] - gt[0] + 1) * (gt[3] - gt[1] + 1) +\n            (pr[2] - pr[0] + 1) * (pr[3] - pr[1] + 1) -\n            overlap_area\n    )\n    #print(\"IOU Calculated... \"+str(overlap_area / union_area))\n    return overlap_area / union_area\n\n\ndef find_best_match(gts, pred, pred_idx, threshold = 0.5, form = 'pascal_voc', ious=None) -> int:\n    # https://www.kaggle.com/sadmanaraf/wheat-detection-using-faster-rcnn-train\n    \"\"\"Returns the index of the 'best match' between the\n    ground-truth boxes and the prediction. The 'best match'\n    is the highest IoU. (0.0 IoUs are ignored).\n\n    Args:\n        gts: (List[List[Union[int, float]]]) Coordinates of the available ground-truth boxes\n        pred: (List[Union[int, float]]) Coordinates of the predicted box\n        pred_idx: (int) Index of the current predicted box\n        threshold: (float) Threshold\n        form: (str) Format of the coordinates\n        ious: (np.ndarray) len(gts) x len(preds) matrix for storing calculated ious.\n\n    Return:\n        (int) Index of the best match GT box (-1 if no match above threshold)\n    \"\"\"\n    best_match_iou = -np.inf\n    best_match_idx = -1\n    for gt_idx in range(len(gts)):\n        \n        if gts[gt_idx][0] < 0:\n            # Already matched GT-box\n            continue\n        \n        iou = -1 if ious is None else ious[gt_idx][pred_idx]\n\n        if iou < 0:\n            iou = calculate_iou(gts[gt_idx], pred, form=form)\n            \n            if ious is not None:\n                ious[gt_idx][pred_idx] = iou\n\n        if iou < threshold:\n            continue\n\n        if iou > best_match_iou:\n            best_match_iou = iou\n            best_match_idx = gt_idx\n\n    return best_match_idx\n\ndef calculate_precision(gts, preds, threshold = 0.5, form = 'coco', ious=None) -> float:\n    # https://www.kaggle.com/sadmanaraf/wheat-detection-using-faster-rcnn-train\n    \"\"\"Calculates precision for GT - prediction pairs at one threshold.\n\n    Args:\n        gts: (List[List[Union[int, float]]]) Coordinates of the available ground-truth boxes\n        preds: (List[List[Union[int, float]]]) Coordinates of the predicted boxes,\n               sorted by confidence value (descending)\n        threshold: (float) Threshold\n        form: (str) Format of the coordinates\n        ious: (np.ndarray) len(gts) x len(preds) matrix for storing calculated ious.\n\n    Return:\n        (float) Precision\n    \"\"\"\n    n = len(preds)\n    tp = 0\n    fp = 0\n    fn = 0\n    tn = 0\n    \n    for pred_idx in range(n):\n\n        best_match_gt_idx = find_best_match(gts, preds[pred_idx], pred_idx,\n                                            threshold=threshold, form=form, ious=ious)\n\n        if best_match_gt_idx >= 0:\n            # True positive: The predicted box matches a gt box with an IoU above the threshold.\n            tp += 1\n            # Remove the matched GT box\n            gts[best_match_gt_idx] = -1\n        else:\n            # No match\n            # False positive: indicates a predicted box had no associated gt box.\n            fp += 1\n\n    # False negative: indicates a gt box had no associated predicted box.\n    \n    fn = (gts.sum(axis=1) > 0).sum()\n    tn = (gts.sum(axis=1) < 0).sum()\n\n    return tp / (tp + fp + fn) ,tp ,fp ,fn ,tn\n\n\n# Albumentations\ndef get_train_transform():\n    return A.Compose([\n        A.Flip(0.5),\n        A.RandomRotate90(0.5),\n        MotionBlur(p=0.2),\n        MedianBlur(blur_limit=3, p=0.1),\n        Blur(blur_limit=3, p=0.1),\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\ndef get_valid_transform():\n    return A.Compose([\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\ndef prepare_data():\n    DIR_INPUT = '../input/rsna-pneumonia-detection-2018/input'\n    DIR_TRAIN = f\"{DIR_INPUT}/images/\"\n\n    train_df = pd.read_csv(f\"{DIR_INPUT}/stage_2_train_labels.csv\")\n    print(train_df.shape)\n    train_df.head()\n\n    train_df_pos = pd.DataFrame(columns=['patientId', 'x', 'y', 'width', 'height'])\n\n    k = 0\n    for i in range(len(train_df)):\n        if train_df.loc[i]['Target'] == 1:\n            train_df_pos.loc[k] = train_df.loc[i]\n            k += 1\n\n    image_ids = train_df_pos['patientId'].unique()\n    valid_ids = image_ids[-300:]\n    train_ids = image_ids[:-300]\n    print(f\"Training instance: {len(train_ids)}\")\n    print(f\"Validation instances: {len(valid_ids)}\")\n\n    valid_df = train_df_pos[train_df_pos['patientId'].isin(valid_ids)]\n    train_df = train_df_pos[train_df_pos['patientId'].isin(train_ids)]\n\n    valid_df.shape, train_df.shape\n    \n    train_dataset = dataset.RSNADataset(train_df, DIR_TRAIN, get_train_transform())\n    valid_dataset = dataset.RSNADataset(valid_df, DIR_TRAIN, get_valid_transform())\n    \n    return train_dataset, valid_dataset\n    \ndef get_data_loader(batch_size):\n    \n    train_dataset, valid_dataset = prepare_data()\n    \n    train_data_loader = DataLoader(\n        train_dataset,\n        batch_size=batch_size,\n        shuffle=False,\n        num_workers=4, # else showing broken pipe error\n        collate_fn=collate_fn\n    )\n\n    valid_data_loader = DataLoader(\n        valid_dataset,\n        batch_size=batch_size,\n        shuffle=False,\n        num_workers=4, # else showing broken pipe error\n        collate_fn=collate_fn\n    )\n    return train_data_loader, valid_data_loader\n\nclass Averager:\n    def __init__(self):\n        self.current_total = 0.0\n        self.iterations = 0.0\n\n    def send(self, value):\n        self.current_total += value\n        self.iterations += 1\n\n    @property\n    def value(self):\n        if self.iterations == 0:\n            return 0\n        else:\n            return 1.0 * self.current_total / self.iterations\n\n    def reset(self):\n        self.current_total = 0.0\n        self.iterations = 0.0\n        \ndef train(dataloader, lr_scheduler, model, optimizer, \n          device, epoch, loss_hist, itr):\n    model.train()\n    start = time.time()\n    loss_hist.reset()\n    for images, targets, image_ids in dataloader:\n        \n        images = list(image.to(device) for image in images)\n        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n        loss_dict = model(images, targets)\n\n\n        losses = sum(loss for loss in loss_dict.values())\n        loss_value = losses.item()\n\n        loss_hist.send(loss_value)\n\n        optimizer.zero_grad()\n        losses.backward()\n        optimizer.step()\n\n        if itr % 50 == 0:\n            print(f\"Epoch #{epoch} iteration #{itr} loss: {loss_value}\")\n\n        itr += 1\n    \n    end = time.time()\n    return loss_hist, end, start\n\ndef validate(dataloader, model, device, iou_thresholds):\n    valid_image_precision = []\n    valid_accuracy = []\n    v_TP, v_FP, v_FN, v_TN = [],[],[],[]\n    model.eval()\n    with torch.no_grad():\n        for images, targets, image_ids in dataloader:\n\n            images = list(image.to(device) for image in images)\n            targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n            outputs = model(images)\n    #print(outputs)\n    for i, image in enumerate(images):\n        boxes = outputs[i]['boxes'].data.cpu().numpy()\n        scores = outputs[i]['scores'].data.cpu().numpy()\n        gt_boxes = targets[i]['boxes'].cpu().numpy()\n        preds_sorted_idx = np.argsort(scores)[::-1]\n        preds_sorted = boxes[preds_sorted_idx]\n        image_precision, TP, FP, FN,TN, accuracy = calculate_image_precision(preds_sorted,\n                                                        gt_boxes,\n                                                        thresholds=iou_thresholds,\n                                                        form='coco')\n        valid_image_precision.append(image_precision)\n        valid_accuracy.append(accuracy)\n        v_TP.append(TP)\n        v_FP.append(FP)\n        v_FN.append(FN)\n        v_TN.append(TN)\n    valid_prec = np.mean(valid_image_precision)\n    valid_acc = np.mean(valid_accuracy)\n    cmat = [[v_TP, v_FN], [v_FP, v_TN]]\n    return valid_prec, cmat, valid_acc","metadata":{"execution":{"iopub.status.busy":"2021-07-14T17:30:29.122145Z","iopub.execute_input":"2021-07-14T17:30:29.122495Z","iopub.status.idle":"2021-07-14T17:30:29.134032Z","shell.execute_reply.started":"2021-07-14T17:30:29.122466Z","shell.execute_reply":"2021-07-14T17:30:29.132959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile train.py\n\nimport torch\nimport engine\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport cv2\nimport seaborn as sns\nfrom engine import get_data_loader, Averager, train, validate\nfrom model import model\n# from torch.utils.data.sampler import SequentialSampler\n\nmatplotlib.style.use('ggplot')\n\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-s', '--show-sample', dest='show_sample', default='no', \n                 help='whether to visualize a wheat sample with bboxes or not')\nargs = vars(parser.parse_args())\n\n# learning parameters\nnum_epochs = 1\nlr = 0.001\nbatch_size = 8\n\nmodel = model().to(device)\nparams = [p for p in model.parameters() if p.requires_grad]\noptimizer = torch.optim.SGD(params, lr=lr, momentum=0.9, weight_decay=0.0005)\n# optimizer = torch.optim.Adam(params, lr=0.01)\n# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)\nlr_scheduler = None\n\n# initialize the Averager\nloss_hist = engine.Averager()\n# get the dataloader\ntrain_data_loader, valid_data_loader = get_data_loader(batch_size)\n\nif args['show_sample'] == 'yes':\n    images, targets, image_ids = next(iter(train_data_loader))\n    images = list(image.to(device) for image in images)\n    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n    boxes = targets[2]['boxes'].cpu().numpy().astype(np.int32)\n    sample = images[2].permute(1,2,0).cpu().numpy()\n    fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n\n    for box in boxes:\n        cv2.rectangle(sample,\n                      (box[0], box[1]),\n                      (box[2], box[3]),\n                      (220, 0, 0), 3)\n    \n    ax.set_axis_off()\n    ax.imshow(sample)\n    plt.show()\n\niou_thresholds = [x for x in np.arange(0.5, 0.76, 0.05)]\n\ntrain_loss = []\nprecision = []\nfor epoch in range(num_epochs):\n    itr = 1\n    train_loss_hist, end, start = train(train_data_loader, lr_scheduler,\n                                        model, optimizer, device,\n                                        epoch, loss_hist, itr)\n    valid_prec, cmat, valid_acc = validate(valid_data_loader, model, device, iou_thresholds)\n    plt.figure(figsize = (6,6))\n    sns.heatmap(cmat/np.sum(cmat), cmap=\"Reds\", annot=True, fmt = '.2%', square=1,   linewidth=2.)\n    plt.xlabel(\"predictions\")\n    plt.ylabel(\"real values\")\n    plt.show()\n    print(f\"Took {(end-start)/60:.3f} minutes for epoch# {epoch} to train\")\n    print(f\"Epoch #{epoch} Train loss: {train_loss_hist.value}\")  \n    print(f\"Epoch #{epoch} Validation Precision: {valid_prec}\")  \n    train_loss.append(train_loss_hist.value)\n    precision.append(valid_prec)\n    \n    # update the learning rate\n    if lr_scheduler is not None:\n        lr_scheduler.step()\n\ntorch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')\n\n# plot and save the training loss\nplt.figure()\nplt.plot(train_loss, label='Training loss')\nplt.legend()\nplt.show()\nplt.savefig('loss.png')\n\n# plot and save the validation precision\nplt.figure()\nplt.plot(precision, label='Validation precision')\nplt.legend()\nplt.show()\nplt.savefig('precision.png')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-07-14T17:30:30.046585Z","iopub.execute_input":"2021-07-14T17:30:30.046900Z","iopub.status.idle":"2021-07-14T17:30:30.052684Z","shell.execute_reply.started":"2021-07-14T17:30:30.046869Z","shell.execute_reply":"2021-07-14T17:30:30.051865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python train.py --show-sample yes","metadata":{"execution":{"iopub.status.busy":"2021-07-14T17:30:30.849229Z","iopub.execute_input":"2021-07-14T17:30:30.849546Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!zip fasterrcnn.zip ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile test.py\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\nimport albumentations as A\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGeneratoar\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\nfrom PIL import Image\nfrom albumentations.pytorch.transforms import ToTensorV2\nfrom matplotlib import pyplot as plt\nfrom tqdm import tqdm\n\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\nDIR_INPUT = '../input/rsna-pneumonia-detection-2018/input'\nDIR_TEST = f\"{DIR_INPUT}/samples\"\ntest_images = os.listdir(DIR_TEST)\nprint(f\"Validation instances: {len(test_images)}\")\n\n# load a model; pre-trained on COCO\nmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, min_size=1024)\nnum_classes = 2  # 1 class (pnueomonia) + background\n# get the number of input features for the classifier\nin_features = model.roi_heads.box_predictor.cls_score.in_features\n# replace the pre-trained head with a new one\nmodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\nos.makedirs('../validation_predictions', exist_ok=True)\nmodel.load_state_dict(torch.load('../input/rsna-pytorch-hackathon-fasterrcnn-resnet-training/fasterrcnn_resnet50_fpn.pth'))\nmodel.to(device)\n\ndef format_prediction_string(boxes, scores):\n    pred_strings = []\n    for j in zip(scores, boxes):\n        pred_strings.append(\"{0:.4f} {1} {2} {3} {4}\".format(j[0], \n                                                             int(j[1][0]), int(j[1][1]), \n                                                             int(j[1][2]), int(j[1][3])))\n\n    return \" \".join(pred_strings)\n\ndetection_threshold = 0.9\nimg_num = 0\nresults = []\nmodel.eval()\nwith torch.no_grad():\n    for i, image in tqdm(enumerate(test_images), total=len(test_images)):\n\n        orig_image = cv2.imread(f\"{DIR_TEST}/{test_images[i]}\", cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n        image = np.transpose(image, (2, 0, 1)).astype(np.float)\n        image = torch.tensor(image, dtype=torch.float).cuda()\n        image = torch.unsqueeze(image, 0)\n\n        model.eval()\n        cpu_device = torch.device(\"cpu\")\n\n        outputs = model(image)\n        \n        outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]\n        if len(outputs[0]['boxes']) != 0:\n            for counter in range(len(outputs[0]['boxes'])):\n                boxes = outputs[0]['boxes'].data.cpu().numpy()\n                scores = outputs[0]['scores'].data.cpu().numpy()\n                boxes = boxes[scores >= detection_threshold].astype(np.int32)\n                draw_boxes = boxes.copy()\n                boxes[:, 2] = boxes[:, 2] - boxes[:, 0]\n                boxes[:, 3] = boxes[:, 3] - boxes[:, 1]\n                \n            for box in draw_boxes:\n                cv2.rectangle(orig_image,\n                            (int(box[0]), int(box[1])),\n                            (int(box[2]), int(box[3])),\n                            (0, 0, 255), 3)\n        \n            plt.imshow(cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB))\n            plt.axis('off')\n            plt.savefig(f\"{test_images[i]}\")\n            plt.close()\n                \n            result = {\n                'patientId': test_images[i].split('.')[0],\n                'PredictionString': format_prediction_string(boxes, scores)\n            }\n            results.append(result)\n        else:\n            result = {\n                'patientId': test_images[i].split('.')[0],\n                'PredictionString': None\n            }\n            results.append(result)\n\nsub_df = pd.DataFrame(results, columns=['patientId', 'PredictionString'])\nprint(sub_df.head())\nsub_df.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2021-07-08T15:13:00.663467Z","iopub.execute_input":"2021-07-08T15:13:00.663737Z","iopub.status.idle":"2021-07-08T15:13:00.669829Z","shell.execute_reply.started":"2021-07-08T15:13:00.663713Z","shell.execute_reply":"2021-07-08T15:13:00.668938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python test.py","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from IPython.display import Image\nImage(filename='./loss.png')\n","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:07:14.574627Z","iopub.execute_input":"2021-06-20T16:07:14.574938Z","iopub.status.idle":"2021-06-20T16:07:14.583271Z","shell.execute_reply.started":"2021-06-20T16:07:14.574911Z","shell.execute_reply":"2021-06-20T16:07:14.582258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Image(filename='./precision.png')","metadata":{"execution":{"iopub.status.busy":"2021-06-20T16:07:19.983048Z","iopub.execute_input":"2021-06-20T16:07:19.983372Z","iopub.status.idle":"2021-06-20T16:07:19.990119Z","shell.execute_reply.started":"2021-06-20T16:07:19.983341Z","shell.execute_reply":"2021-06-20T16:07:19.989046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Model III : DenseNet**","metadata":{}},{"cell_type":"code","source":"LR = 0.005\nEPOCHS = 2\nBATCHSIZE = 32\nCHANNELS = 64\nIMAGE_SIZE = 256\nNBLOCK = 6 \nDEPTH = 2\nMOMENTUM = 0.9\n\nfrom skimage import measure\nfrom skimage.transform import resize\n\nimport tensorflow as tf\nfrom tensorflow import keras\nimport os\nfrom matplotlib import pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:53:50.787435Z","iopub.execute_input":"2021-06-20T14:53:50.787862Z","iopub.status.idle":"2021-06-20T14:53:50.79482Z","shell.execute_reply.started":"2021-06-20T14:53:50.787816Z","shell.execute_reply":"2021-06-20T14:53:50.793262Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import csv\nimport random\n# Load pneumonia locations\n\n# empty dictionary\npneumonia_locations = {}\n# load table\nwith open(os.path.join('../input/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv'), mode='r') as infile:\n    # open reader\n    reader = csv.reader(infile)\n    # skip header\n    next(reader, None)\n    # loop through rows\n    for rows in reader:\n        # retrieve information\n        filename = rows[0]\n        location = rows[1:5]\n        pneumonia = rows[5]\n        # if row contains pneumonia add label to dictionary\n        # which contains a list of pneumonia locations per filename\n        if pneumonia == '1':\n            # convert string to float to int\n            location = [int(float(i)) for i in location]\n            # save pneumonia location in dictionary\n            if filename in pneumonia_locations:\n                pneumonia_locations[filename].append(location)\n            else:\n                pneumonia_locations[filename] = [location]\n                \n                \n# Load filenames\n\n# load and shuffle filenames\nfolder = '../input/rsna-pneumonia-detection-challenge/stage_2_train_images'\nfilenames = os.listdir(folder)\nfilenames = filenames[:2000]\nrandom.shuffle(filenames)\n# split into train and validation filenames\nn_valid_samples = 2560\ntrain_filenames = filenames[:1800]\nvalid_filenames = filenames[1800:]\nprint('n train samples', len(train_filenames))\nprint('n valid samples', len(valid_filenames))\nn_train_samples = len(filenames) - n_valid_samples","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:53:55.550704Z","iopub.execute_input":"2021-06-20T14:53:55.551309Z","iopub.status.idle":"2021-06-20T14:53:56.689994Z","shell.execute_reply.started":"2021-06-20T14:53:55.551259Z","shell.execute_reply":"2021-06-20T14:53:56.688601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Data generator\n\nclass generator(keras.utils.Sequence):\n    \n    def __init__(self, folder, filenames, pneumonia_locations=None, batch_size=BATCHSIZE, \n                 image_size=IMAGE_SIZE, shuffle=True, augment=False, predict=False):\n        self.folder = folder\n        self.filenames = filenames\n        self.pneumonia_locations = pneumonia_locations\n        self.batch_size = batch_size\n        self.image_size = image_size\n        self.shuffle = shuffle\n        self.augment = augment\n        self.predict = predict\n        self.on_epoch_end()\n        \n    def __load__(self, filename):\n        # load dicom file as numpy array\n        img = dcm.dcmread(os.path.join(self.folder, filename)).pixel_array\n        # default negative\n        target = 0\n        # get filename without extension\n        filename = filename.split('.')[0]\n        # if image contains pneumonia\n        if filename in pneumonia_locations:\n            target = 1\n        # resize both image and mask\n        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n        # if augment then horizontal flip half the time\n        if self.augment and random.random() > 0.5:\n            img = np.fliplr(img)\n        # add trailing channel dimension\n        img = np.expand_dims(img, -1)\n        return img, target\n    \n    def __loadpredict__(self, filename):\n        # load dicom file as numpy array\n        img = dcm.dcmread(os.path.join(self.folder, filename)).pixel_array\n        # resize image\n        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n        # add trailing channel dimension\n        img = np.expand_dims(img, -1)\n        return img\n        \n    def __getitem__(self, index):\n        # select batch\n        filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]\n        # predict mode: return images and filenames\n        if self.predict:\n            # load files\n            imgs = [self.__loadpredict__(filename) for filename in filenames]\n            # create numpy batch\n            imgs = np.array(imgs)\n            return imgs, filenames\n        # train mode: return images and masks\n        else:\n            # load files\n            items = [self.__load__(filename) for filename in filenames]\n            # unzip images and masks\n            imgs, targets = zip(*items)\n            # create numpy batch\n            imgs = np.array(imgs)\n            targets = np.array(targets)\n            return imgs, targets\n        \n    def on_epoch_end(self):\n        if self.shuffle:\n            random.shuffle(self.filenames)\n        \n    def __len__(self):\n        if self.predict:\n            # return everything\n            return int(np.ceil(len(self.filenames) / self.batch_size))\n        else:\n            # return full batches only\n            return int(len(self.filenames) / self.batch_size)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:54:02.451817Z","iopub.execute_input":"2021-06-20T14:54:02.452293Z","iopub.status.idle":"2021-06-20T14:54:02.470548Z","shell.execute_reply.started":"2021-06-20T14:54:02.452261Z","shell.execute_reply":"2021-06-20T14:54:02.467594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Network\n\ndef convlayer(channels, inputs, size=3, padding='same'):\n    x = keras.layers.BatchNormalization(momentum=MOMENTUM)(inputs)\n    x = keras.layers.LeakyReLU(0)(x)\n    x = keras.layers.Conv2D(channels, size, padding=padding, use_bias=False)(x)\n    return x\n\ndef just_downsample(inputs, pool=2):\n    x = keras.layers.BatchNormalization(momentum=MOMENTUM)(inputs)\n    x = keras.layers.LeakyReLU(0)(x)\n    x = keras.layers.MaxPool2D(pool)(x)\n    return x\n\ndef convblock(inputs, channels1, channels2):\n    x = convlayer(channels1, inputs)\n    x = convlayer(channels2, x)\n    x = keras.layers.Concatenate()([inputs, x])\n    return x\n\ndef denseblock(inputs, nblocks=6, channels1=128, channels2=32):\n    x = inputs\n    for i in range(nblocks):\n        x = convblock(x, channels1, channels2)\n    x = keras.layers.SpatialDropout2D(.2)(x)\n    return x\n\ndef transition(inputs, channels, pool=2):\n    x = convlayer(channels, inputs)\n    x = keras.layers.AveragePooling2D(pool)(x)\n    return x\n    \ndef create_network(input_size, channels=64, channels2=32, n_blocks=NBLOCK, depth=DEPTH):\n    # input\n    inputs = keras.Input(shape=(input_size, input_size, 1))\n    x = keras.layers.Conv2D(channels, 3, padding='same', strides=2, use_bias=False)(inputs)\n    x = just_downsample(x)\n\n    # densenet blocks\n    nchan = channels\n    for d in range(depth-1):\n        x = denseblock(x)\n        nchan = ( nchan + n_blocks*channels2 ) // 2\n        x = transition(x, nchan)\n    x = denseblock(x)\n\n    # output\n    x = convlayer(channels, x)\n    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n    x = keras.layers.GlobalAveragePooling2D()(x)\n    x = keras.layers.LeakyReLU(0)(x)\n    x = keras.layers.Dropout(.5)(x)\n    output = keras.layers.Dense(1, activation='sigmoid')(x)\n    model = keras.Model(inputs=inputs, outputs=output)\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:54:08.114638Z","iopub.execute_input":"2021-06-20T14:54:08.115051Z","iopub.status.idle":"2021-06-20T14:54:08.132432Z","shell.execute_reply.started":"2021-06-20T14:54:08.115008Z","shell.execute_reply":"2021-06-20T14:54:08.130334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create network and compiler\nmodel = create_network(input_size=IMAGE_SIZE, channels=CHANNELS, n_blocks=NBLOCK, depth=DEPTH)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:54:12.010386Z","iopub.execute_input":"2021-06-20T14:54:12.011019Z","iopub.status.idle":"2021-06-20T14:54:15.101423Z","shell.execute_reply.started":"2021-06-20T14:54:12.010967Z","shell.execute_reply":"2021-06-20T14:54:15.100302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer=keras.optimizers.Adam(lr=LR),\n              loss=keras.losses.binary_crossentropy, metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:54:15.105104Z","iopub.execute_input":"2021-06-20T14:54:15.105452Z","iopub.status.idle":"2021-06-20T14:54:15.128343Z","shell.execute_reply.started":"2021-06-20T14:54:15.105421Z","shell.execute_reply":"2021-06-20T14:54:15.126967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_gen = generator(folder, train_filenames, pneumonia_locations, batch_size=BATCHSIZE, \n                      image_size=IMAGE_SIZE, shuffle=True, augment=True, predict=False)\nvalid_gen = generator(folder, valid_filenames, pneumonia_locations, batch_size=BATCHSIZE, \n                      image_size=IMAGE_SIZE, shuffle=False, predict=False)\n\nhistory = model.fit_generator(train_gen, validation_data=valid_gen, \n                              epochs=EPOCHS, shuffle=True, verbose=2)","metadata":{"execution":{"iopub.status.busy":"2021-06-20T14:54:47.999605Z","iopub.execute_input":"2021-06-20T14:54:48.000001Z","iopub.status.idle":"2021-06-20T14:59:58.217494Z","shell.execute_reply.started":"2021-06-20T14:54:47.999967Z","shell.execute_reply":"2021-06-20T14:59:58.21583Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-06-20T15:13:29.897636Z","iopub.execute_input":"2021-06-20T15:13:29.898047Z","iopub.status.idle":"2021-06-20T15:13:30.096532Z","shell.execute_reply.started":"2021-06-20T15:13:29.898012Z","shell.execute_reply":"2021-06-20T15:13:30.094869Z"},"trusted":true},"execution_count":null,"outputs":[]}]}