{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":10338,"databundleVersionId":862042,"sourceType":"competition"},{"sourceId":9864328,"sourceType":"datasetVersion","datasetId":6054580},{"sourceId":169572,"sourceType":"modelInstanceVersion","isSourceIdPinned":true,"modelInstanceId":142732,"modelId":164031}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Introduction**\n\nPneumonia is a serious respiratory infection that can cause inflammation of the air sacs in the lungs, leading to symptoms such as cough, chest pain, fever, and difficulty breathing. Early detection is critical for effective treatment, as pneumonia can progress rapidly and lead to severe complications if left untreated. Traditionally, diagnosing pneumonia involves the interpretation of medical imaging, such as **chest X-rays** or **CT scans**, by radiologists. However, the increasing volume of medical imaging data and the shortage of skilled radiologists have led to an increased interest in automated methods for **pneumonia detection**.\n\n### **ResNet-50 for Pneumonia Detection**\n\n**ResNet-50** is a deep convolutional neural network that uses **residual learning** to enable the training of very deep networks. It is part of the **ResNet family of architectures**, which are designed to address the problem of vanishing gradients in deep networks. The main innovation in ResNet is the introduction of **skip connections** or **residual connections**, which allow gradients to flow more easily through the network during backpropagation. This makes it possible to train very deep models with hundreds or even thousands of layers without the degradation of performance due to training difficulties.\n\nResNet-50 specifically refers to a ResNet model with 50 layers, which strikes a balance between model depth and computational efficiency. It is widely used in image classification tasks and has achieved state-of-the-art results in several benchmark datasets, including the **ImageNet** challenge. In the context of pneumonia detection, ResNet-50 is particularly effective because it can learn complex features from chest X-ray images, such as lung opacities and other pneumonia-related abnormalities, enabling it to classify whether a given image contains signs of pneumonia.\n\nFor a more detailed explanation of how ResNet works, including the core concepts of residual connections, skip connections, and how the architecture is designed to overcome the limitations of traditional CNNs, you can refer to the article **[Introduction to ResNet-18](https://www.kaggle.com/code/iamtapendu/introduction-to-resnet-18)**. Although the article focuses on ResNet-18, it provides a comprehensive overview of residual networks that can help you understand the underlying principles of ResNet-50.","metadata":{}},{"cell_type":"markdown","source":"## **Importing Libraries**","metadata":{}},{"cell_type":"code","source":"# TensorFlow and Keras Imports\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers,models\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom tensorflow.keras.applications import ResNet50\n\n# Image Processing Libraries\nimport cv2\n\n# Data Handling Libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils.class_weight import compute_class_weight\n\n# Visualization Libraries\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# File and Operating System Libraries\nimport os\n\n# Warnings Management\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# GPU Configuration\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nprint(tf.config.list_physical_devices('GPU'))\n\nIMG_PATH = '/kaggle/input/rsna-pneumonia-processed-dataset/Training/Images/'\nMODEL_PATH = '/kaggle/input/pneumonia-detection-using-resnet50/tensorflow2/model-v1.1/2/lung_detection_2.keras'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:37.286885Z","iopub.execute_input":"2024-11-17T13:27:37.287591Z","iopub.status.idle":"2024-11-17T13:27:50.469619Z","shell.execute_reply.started":"2024-11-17T13:27:37.287539Z","shell.execute_reply":"2024-11-17T13:27:50.468776Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Dataset Overview for Pneumonia Detection Challenge**\n\n#### **Goal**: \nBuild an algorithm to identify pneumonia in chest X-rays by locating lung opacities.\n\n- **Total Rows**: 30,227\n- **Unique Patients**: 26,684\n\n### **Folder Structure**\n\n- **Train**\n  - **Images:** Contains the processed PNG images for the training set.\n  - **Masks:** Folder containing binary masks corresponding to the bounding boxes for the training set.\n- **Test**: Contains the processed PNG images for the test set.\n- **Train_metadata.csv**: Metadata for the training set, including patient IDs, bounding box coordinates, and labels.\n- **Test_metadata.csv**: Metadata for the test set, including patient IDs.\n\n### **Key Data Columns**\n- **patientId:** Unique identifier for each patient/image.\n- **x:** The x-coordinate of the top-left corner of the bounding box.\n- **y:** The y-coordinate of the top-left corner of the bounding box.\n- **width:** The width of the bounding box.\n- **height:** The height of the bounding box.\n- **Target:** Binary label indicating the presence of pneumonia (1 for pneumonia, 0 for no pneumonia).\n- **class:** The class/category of pneumonia (e.g. Lung Opacity, Normal, No Lung Opacity / Not Normal ).\n- **age:** The age of the patient at the time the image was taken.\n- **sex:** The sex/gender of the patient (e.g., Male or Female).\n- **modality:** The imaging modality used (e.g., X-ray, CT scan).\n- **position:** The position of the patient during imaging (e.g., AP for anterior-posterior, PA for posterior-anterior).\n\n### **Distribution**\n- **No Lung Opacity / Not Normal:** 11,821 samples\n- **Normal:** 8,851 samples\n- **Lung Opacity:** 9,555 samples\n\n","metadata":{},"attachments":{"7b48fc6d-b520-41e6-9ae7-51e0f0a384e4.png":{"image/png":"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"}}},{"cell_type":"code","source":"train_metadata = pd.read_csv('/kaggle/input/rsna-pneumonia-processed-dataset/stage2_train_metadata.csv')\ntest_metadata = pd.read_csv('/kaggle/input/rsna-pneumonia-processed-dataset/stage2_test_metadata.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.471527Z","iopub.execute_input":"2024-11-17T13:27:50.472219Z","iopub.status.idle":"2024-11-17T13:27:50.590505Z","shell.execute_reply.started":"2024-11-17T13:27:50.472172Z","shell.execute_reply":"2024-11-17T13:27:50.589522Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_metadata.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.591871Z","iopub.execute_input":"2024-11-17T13:27:50.592275Z","iopub.status.idle":"2024-11-17T13:27:50.632983Z","shell.execute_reply.started":"2024-11-17T13:27:50.592230Z","shell.execute_reply":"2024-11-17T13:27:50.632071Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_metadata.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.634770Z","iopub.execute_input":"2024-11-17T13:27:50.635075Z","iopub.status.idle":"2024-11-17T13:27:50.646180Z","shell.execute_reply.started":"2024-11-17T13:27:50.635044Z","shell.execute_reply":"2024-11-17T13:27:50.645140Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_metadata.describe().style.background_gradient()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.647213Z","iopub.execute_input":"2024-11-17T13:27:50.647484Z","iopub.status.idle":"2024-11-17T13:27:50.764274Z","shell.execute_reply.started":"2024-11-17T13:27:50.647455Z","shell.execute_reply":"2024-11-17T13:27:50.763378Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_metadata.describe(include='O')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.765469Z","iopub.execute_input":"2024-11-17T13:27:50.765990Z","iopub.status.idle":"2024-11-17T13:27:50.833427Z","shell.execute_reply.started":"2024-11-17T13:27:50.765956Z","shell.execute_reply":"2024-11-17T13:27:50.832502Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Total rows in train_labels:',train_metadata.shape[0])\nprint('Total unique patients in train_labels:',train_metadata['patientId'].nunique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:27:50.834488Z","iopub.execute_input":"2024-11-17T13:27:50.834802Z","iopub.status.idle":"2024-11-17T13:27:50.847789Z","shell.execute_reply.started":"2024-11-17T13:27:50.834770Z","shell.execute_reply":"2024-11-17T13:27:50.846732Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(14,10))\nsns.set_palette('rocket_r')\n\nplt.subplot(221)\nagg_data = train_metadata['class'].value_counts()\nplt.pie(agg_data,autopct='%4.2f',labels=agg_data.index)\n\nplt.subplot(222)\nsns.boxplot(train_metadata,x='class',y='age')\nplt.yticks([])\nplt.box(False)\n\nplt.subplot(223)\nsns.countplot(train_metadata,x='class',hue='sex')\nplt.box(False)\n\nplt.subplot(224)\nsns.countplot(train_metadata,x='class',hue='position')\n# plt.yticks([])\nplt.box(False)\n\nplt.suptitle('Data Overview',fontsize=20)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:30:29.421459Z","iopub.execute_input":"2024-11-17T13:30:29.421830Z","iopub.status.idle":"2024-11-17T13:30:30.179231Z","shell.execute_reply.started":"2024-11-17T13:30:29.421797Z","shell.execute_reply":"2024-11-17T13:30:30.178254Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Data Processing**","metadata":{}},{"cell_type":"code","source":"train_metadata.drop(['x','y','width','height'],axis=1,inplace=True)\ntrain_metadata.drop_duplicates(inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:30:35.338119Z","iopub.execute_input":"2024-11-17T13:30:35.338514Z","iopub.status.idle":"2024-11-17T13:30:35.369346Z","shell.execute_reply.started":"2024-11-17T13:30:35.338476Z","shell.execute_reply":"2024-11-17T13:30:35.368589Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Split the unique patient IDs into training and validation sets\ntrain_patient_id, val_patient_id, train_target, val_target = train_test_split(\n    train_metadata.patientId,  # Features to split\n    train_metadata['Target'],     # Target labels to stratify by\n    test_size=0.1,  # 10% of the data will be used for validation\n    stratify=train_metadata['Target'],  # Ensure that the split maintains the proportion of each class\n    random_state=123  # Set a seed for reproducibility\n)\n\n# Compute class weights based on the training labels\nclass_weights = compute_class_weight(\n    'balanced',  # This will automatically adjust for class imbalance\n    classes=np.unique(train_metadata.Target),  # Unique classes in the target\n    y=train_metadata.Target  # The target class labels for training\n)\n\n# Create a dictionary mapping each class to its weight\nclass_weight_dict = {i: class_weights[i] for i in range(len(class_weights))}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:30:35.395127Z","iopub.execute_input":"2024-11-17T13:30:35.395725Z","iopub.status.idle":"2024-11-17T13:30:35.419639Z","shell.execute_reply.started":"2024-11-17T13:30:35.395687Z","shell.execute_reply":"2024-11-17T13:30:35.418731Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Custom Data Generator**","metadata":{}},{"cell_type":"code","source":"class DataGenerator(keras.utils.Sequence):\n    def __init__(self, patient_id, target_class, batch_size=32, size=(512, 512), seed=1, shuffle=True, **kwargs):\n        \"\"\"\n        Custom data generator for segmentation tasks.\n        \n        Args:\n        - img_dir: Directory containing the input images\n        - mask_dir: Directory containing the corresponding masks\n        - batch_size: Number of samples per batch\n        - size: The target size for resizing the images and masks\n        - seed: Random seed for reproducibility\n        - shuffle: Whether to shuffle the dataset after each epoch\n        \"\"\"\n        super().__init__(**kwargs)\n        \n        # List image and mask files\n        self.patient_id = patient_id\n        self.target_class = target_class\n        \n        self.batch_size = batch_size\n        self.size = size\n        self.seed = seed\n        self.shuffle = shuffle\n        \n        # Ensure the number of images matches the number of masks\n        assert len(self.patient_id) == len(self.target_class), \\\n            \"The number of images and masks must be the same\"\n        \n        self.indexes = np.arange(len(self.patient_id))  # Indices for shuffling\n        \n        # If shuffle is enabled, shuffle the indices after each epoch\n        if self.shuffle:\n            self.on_epoch_end()\n\n    def __len__(self):\n        \"\"\"\n        Returns the number of batches per epoch.\n        \"\"\"\n        return int(np.floor(len(self.patient_id) / self.batch_size))\n\n    def __getitem__(self, index):\n        \"\"\"\n        Generates a batch of data (images and corresponding masks).\n        \n        Args:\n        - index: The index of the batch.\n        \n        Returns:\n        - A batch of images and masks\n        \"\"\"\n        # Get batch indices\n        batch_indices = self.indexes[index * self.batch_size : (index + 1) * self.batch_size]\n        \n        # Initialize empty arrays for the batch\n        images = []  \n        target = []\n        \n        for i, idx in enumerate(batch_indices):\n            # Load and preprocess image\n            img = cv2.imread(IMG_PATH+self.patient_id[idx]+'.png',1)  # Read image\n            img = cv2.resize(img, self.size)  # Resize to target size\n            img = img / 255.0  # Normalize to [0, 1]\n            \n            # Add image and mask to the batch arrays\n            images.append(img)\n            target.append(self.target_class[idx])\n        \n        return np.array(images), np.array(target)\n\n    def on_epoch_end(self):\n        \"\"\"\n        Shuffle the dataset after each epoch.\n        \"\"\"\n        if self.shuffle:\n            np.random.shuffle(self.indexes)\n\ntrain_data = DataGenerator(\n    patient_id=train_patient_id.tolist(),\n    target_class=train_target.tolist(),    \n    batch_size=8,          \n    size=(512, 512),\n    workers=4, \n    use_multiprocessing=True\n)\n\nval_data = DataGenerator(\n    patient_id=val_patient_id.tolist(),\n    target_class=val_target.tolist(),    \n    batch_size=8,          \n    size=(512, 512),\n    workers=4,\n    use_multiprocessing=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:30:40.083580Z","iopub.execute_input":"2024-11-17T13:30:40.084224Z","iopub.status.idle":"2024-11-17T13:30:40.100687Z","shell.execute_reply.started":"2024-11-17T13:30:40.084185Z","shell.execute_reply":"2024-11-17T13:30:40.099688Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **ResNet-50 Model**","metadata":{}},{"cell_type":"code","source":"input_shape = (512,512,3)\nnum_classes = 1\n\nimage_input = layers.Input(shape=input_shape)\n# Pre-trained ResNet model without the top layers\nresnet = ResNet50(weights='imagenet', include_top=False,pooling='avg', input_shape=input_shape)\nx = resnet(image_input)\nx = layers.Flatten()(x)  # Flatten the output of ResNet\nx = layers.Dense(1024, activation='relu')(x)  # Dense layer for image features\nx = layers.Dense(num_classes,activation='sigmoid')(x)\nmodel =  models.Model(inputs=image_input, outputs=x)\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:30:45.823155Z","iopub.execute_input":"2024-11-17T13:30:45.823787Z","iopub.status.idle":"2024-11-17T13:30:52.390296Z","shell.execute_reply.started":"2024-11-17T13:30:45.823749Z","shell.execute_reply":"2024-11-17T13:30:52.389403Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Loading the pre-trained model from the previous version of this notebook for further training.\nmodel = models.load_model(MODEL_PATH)\n\n# Freezing the first few layers for fine tunning\nmodel.layers[0].trainable = False\nfor layer in model.layers[1].layers[:-50]:\n    layer.trainable = False\n\n# Compiling the model with the combined loss and metrics\nmodel.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5), \n              loss = tf.keras.losses.BinaryCrossentropy(from_logits=False),\n              metrics=['accuracy','AUC']) ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:31:16.406329Z","iopub.execute_input":"2024-11-17T13:31:16.407218Z","iopub.status.idle":"2024-11-17T13:31:31.238381Z","shell.execute_reply.started":"2024-11-17T13:31:16.407177Z","shell.execute_reply":"2024-11-17T13:31:31.237395Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Train The Model**","metadata":{}},{"cell_type":"code","source":"## ModelCheckpoint: Save the model with the best validation loss during training\nchkpnt_loss = ModelCheckpoint(\n    'best_model_loss.keras',            # Path to save the model\n    monitor='val_loss',         # Metric to monitor \n    verbose=1,                  # Print messages when saving the model\n    save_best_only=True,        # Save only the best model (with highest metric)\n    mode='min',                 \n    save_weights_only=False,     # Save the entire model (not just weights)\n)\n\nchkpnt_auc = ModelCheckpoint(\n    'best_model_auc.keras',            # Path to save the model\n    monitor='val_AUC',         # Metric to monitor \n    verbose=1,                  # Print messages when saving the model\n    save_best_only=True,        # Save only the best model (with highest metric)\n    mode='max',                 # 'max' means the model with the highest metric score will be saved\n    save_weights_only=False,     # Save the entire model (not just weights)\n)\n\n# Early stopping callback to prevent overfitting\nearly_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n\n# Reduce learning rate if validation loss plateaus\nlr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(\n    monitor='val_loss', factor=0.5, patience=3, min_lr=1e-7\n)\n# Fit the model\nhistory = model.fit(train_data,\n                    validation_data=val_data,\n                    epochs=24,\n                    class_weight=class_weight_dict,\n                    callbacks=[chkpnt_auc,chkpnt_loss,early_stopping,lr_scheduler])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-14T07:16:15.459931Z","iopub.execute_input":"2024-11-14T07:16:15.460411Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Plot training & validation loss values\nplt.figure(figsize=(16,16))\nplt.subplot(221)\nplt.plot(history.history['loss'], label='Train Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.title('Model Loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend()\n\nplt.subplot(222)\nplt.plot(history.history['accuracy'], label='Train Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.title('Model Accuracy')\nplt.ylabel('Metric Value')\nplt.xlabel('Epoch')\nplt.legend()\n\nplt.subplot(223)\nplt.plot(history.history['AUC'], label='Train AUC')\nplt.plot(history.history['val_AUC'], label='Validation AUC')\nplt.title('Model Accuracy')\nplt.ylabel('Metric Value')\nplt.xlabel('Epoch')\nplt.legend()\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_loss, test_accuracy,test_auc = model.evaluate(val_data,steps=len(val_data), verbose=1)\nprint(f\"Test Loss: {test_loss}\")\nprint(f\"Test Accuracy: {test_accuracy}\")\nprint(f\"Test AUC: {test_auc}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:33:31.291257Z","iopub.execute_input":"2024-11-17T13:33:31.292157Z","iopub.status.idle":"2024-11-17T13:35:08.645952Z","shell.execute_reply.started":"2024-11-17T13:33:31.292115Z","shell.execute_reply":"2024-11-17T13:35:08.644821Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for it in range(4):\n    imgs,clss = val_data.__getitem__(it)\n    pred_prob = np.squeeze(model.predict(imgs,verbose=0))\n    pred_cls = (pred_prob>0.5).astype(int)\n    \n    fig, ax = plt.subplots(1,8,figsize=(16,2))\n    ax = ax.flatten()\n    for i,img in enumerate(imgs):\n        ax[i].imshow(img)\n        ax[i].set_xticks([])\n        ax[i].set_yticks([])\n        ax[i].set_title(f'Actual:{clss[i]}\\nPred:{pred_cls[i]}({round(pred_prob[i]*100,2)}%)',fontsize=11)\n    \n    plt.tight_layout()\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T13:35:08.647973Z","iopub.execute_input":"2024-11-17T13:35:08.648305Z","iopub.status.idle":"2024-11-17T13:35:16.340719Z","shell.execute_reply.started":"2024-11-17T13:35:08.648270Z","shell.execute_reply":"2024-11-17T13:35:16.339796Z"}},"outputs":[],"execution_count":null}]}