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Introduction

\n![U-net.jpg](attachment:e746f9a8-a22c-461b-bdb2-f51c9b36763c.jpg)\n\nU-Net is a deep learning architecture specifically designed for biomedical image segmentation. Introduced in 2015 by Olaf Ronneberger et al., it has become a foundational model in the field due to its effectiveness in delineating complex structures in medical images.\n\n##

Key Features

\n1. **Architecture**: U-Net consists of a contracting path (encoder) and an expansive path (decoder), creating a U-shaped structure. The encoder captures context through downsampling, while the decoder enables precise localization by upsampling. Skip connections between corresponding layers in the encoder and decoder allow for the preservation of spatial information, crucial for accurate segmentation.\n2. **Data Efficiency**: U-Net is designed to work well with relatively few training images. This is particularly important in the biomedical field, where labeled data can be scarce. The model uses data augmentation techniques to enhance training and improve generalization.\n3. **Applications**: U-Net is widely used for segmenting various biomedical images, including:\n\n - **MRI and CT scans**: For identifying tumors, lesions, and organ boundaries.\n\n - **Histopathology images**: For detecting cellular structures and classifying tissue types.\n\n - **Cell segmentation**: In microscopy images, for tasks like identifying individual cells and their components.\n4. **Performance**: The architecture has been shown to achieve high accuracy and robustness in various segmentation tasks, often outperforming traditional methods. Its ability to produce precise pixel-wise predictions makes it a preferred choice for medical image 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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, metrics\nfrom tensorflow.keras.utils import Sequence\nfrom tensorflow.keras.callbacks import ModelCheckpoint\n\n# Image Processing Libraries\nimport cv2\nfrom cv2 import imread,resize\nfrom scipy.ndimage import label, find_objects\n\n# Data Handling Libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\n# Visualization Libraries\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom tqdm import tqdm\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\n# PATHS\nIMG_PATH = '/kaggle/input/chest-x-ray-lungs-segmentation/Chest-X-Ray/Chest-X-Ray/image/'\nMSK_PATH = '/kaggle/input/chest-x-ray-lungs-segmentation/Chest-X-Ray/Chest-X-Ray/mask/'","metadata":{"execution":{"iopub.status.busy":"2024-11-08T19:36:01.292739Z","iopub.execute_input":"2024-11-08T19:36:01.293627Z","iopub.status.idle":"2024-11-08T19:36:14.915359Z","shell.execute_reply.started":"2024-11-08T19:36:01.293579Z","shell.execute_reply":"2024-11-08T19:36:14.914304Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

Dataset Overview for Chest X-Ray Lungs Segmentation

\n![banner-img-02.png](attachment:0c82692e-823b-4344-bd88-57256ad84f1f.png)\n\n##

Dataset Description

\nThis dataset comprises **704 chest X-ray images** sourced from two prominent databases: the **Montgomery County Chest X-ray Database** (USA) and the **Shenzhen Chest X-ray Database** (China). It is designed for training and evaluating machine learning models, particularly in the detection of **tuberculosis (TB)**. The dataset includes both **tuberculosis-positive (PTB)** and **normal** chest X-rays, along with additional **clinical metadata** and **lung segmentation masks** for improved training of deep learning models.\n\nWith both **image segmentation** and clinical labels, the dataset is ideally suited for tasks involving **image classification** and **image segmentation**, making it a valuable resource for tuberculosis detection and general lung health analysis.\n\n##

Data Sources

\n1. **Montgomery County Chest X-ray Database** (USA)\n - Originates from the **Montgomery County, Maryland** Tuberculosis screening program.\n - Contains **58 TB-positive** and **80 normal** X-ray images.\n\n2. **Shenzhen Chest X-ray Database** (China)\n - Collected from **out-patient clinics** in **Shenzhen, Guangdong Province**.\n - Includes **336 TB-positive** and **326 normal** X-ray images.\n\n##

Dataset Statistics

\n- **Total Number of Images**: 704 chest X-rays (from both Montgomery and Shenzhen).\n- **County Distribution**:\n - **Shenzhen**: 80% (563 images).\n - **Montgomery**: 20% (141 images).\n- **Tuberculosis Cases (PTB)**:\n - **PTB=1 (Tuberculosis Positive)**: 345 images.\n - **PTB=0 (Normal)**: 359 images.\n\n##

Clinical Metadata Breakdown

\nThe dataset is accompanied by clinical metadata, which provides important information about each X-ray image. The columns in the metadata file (MetaData.csv) include:\n- **id**: A unique identifier for each X-ray image.\n- **gender**: Gender of the patient (male/female).\n- **age**: The age of the patient.\n- **county**: The geographic origin of the X-ray (Shenzhen or Montgomery).\n- **ptb**: Label indicating whether the X-ray shows **tuberculosis (1)** or is **normal (0)**.\n- **remarks**: Additional clinical notes about the patient's condition (e.g., \"secondary PTB\", \"normal\").\n\n##

Data Structure

\nThe dataset is organized into the following directory structure:\n\n```\n/Chest-X-Ray/Chest-X-Ray\n /image/ # Contains all chest X-ray images\n /mask/ # Contains lung segmentation masks (if available)\n /MetaData.csv # Clinical metadata file with patient and X-ray details\n```","metadata":{},"attachments":{"0c82692e-823b-4344-bd88-57256ad84f1f.png":{"image/png":"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"}}},{"cell_type":"code","source":"metadata = pd.read_csv('/kaggle/input/chest-x-ray-lungs-segmentation/MetaData.csv')\nmetadata.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:12.276285Z","iopub.execute_input":"2024-11-08T18:18:12.276859Z","iopub.status.idle":"2024-11-08T18:18:12.323217Z","shell.execute_reply.started":"2024-11-08T18:18:12.276823Z","shell.execute_reply":"2024-11-08T18:18:12.322213Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"metadata.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:12.324548Z","iopub.execute_input":"2024-11-08T18:18:12.324941Z","iopub.status.idle":"2024-11-08T18:18:12.380103Z","shell.execute_reply.started":"2024-11-08T18:18:12.324897Z","shell.execute_reply":"2024-11-08T18:18:12.379021Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"agg_data = metadata.ptb.value_counts()\nct = pd.crosstab(metadata['gender'], metadata['ptb'])\n\nplt.figure(figsize=(12,5))\nplt.subplot(121)\nplt.pie(agg_data,labels=agg_data.index,autopct='%1.1f%%', startangle=90,colors=['darkorchid','tomato'])\n\nplt.subplot(122)\nbars = sns.countplot(metadata,x='ptb',hue='county',palette=['darkorchid','tomato'])\nplt.bar_label(bars.containers[0], label_type='center')\nplt.bar_label(bars.containers[1], label_type='center')\nplt.yticks([])\nplt.xticks([0,1],labels=['Negative','Positive'])\nplt.xlabel('Tuberculosis')\nplt.box(False)\n\nplt.suptitle('PTB Analysis',fontsize=16)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:12.383298Z","iopub.execute_input":"2024-11-08T18:18:12.384024Z","iopub.status.idle":"2024-11-08T18:18:12.829452Z","shell.execute_reply.started":"2024-11-08T18:18:12.383975Z","shell.execute_reply":"2024-11-08T18:18:12.828497Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gender_mapping = {\n 'm': 'male', 'Male,': 'male', 'male35yrs': 'male', 'male,':'male',\n 'f': 'female', 'femal': 'female', 'female24yrs':'female','o':'female','female,':'female'\n}\nmetadata.gender = metadata.gender.str.lower()\nmetadata.gender.replace(gender_mapping,inplace=True)\nagg_data = metadata.gender.value_counts()\nct = pd.crosstab(metadata['gender'], metadata['ptb'])\n\nplt.figure(figsize=(14,10))\nplt.subplot(221)\nplt.pie(agg_data,labels=agg_data.index,autopct='%1.1f%%', startangle=90,colors=['darkorchid','tomato'])\n\nplt.subplot(222)\nbars = plt.bar(agg_data.index,agg_data,color=['darkorchid','tomato'])\nplt.bar_label(bars, labels=[str(height) for height in agg_data],label_type='center')\nplt.yticks([])\nplt.box(False)\n\nplt.subplot(223)\nbars = ct.T.plot(kind='bar', color=['tomato', 'darkorchid'],ax=plt.gca())\nplt.bar_label(bars.containers[0], label_type='center')\nplt.bar_label(bars.containers[1], label_type='center')\nplt.xlabel('Tuberculosis')\nplt.yticks([])\nplt.box(False)\n\nplt.subplot(224)\nsns.heatmap(ct,annot=True,cbar=False,fmt='d',cmap='viridis')\n\nplt.suptitle('Gender Analysis',fontsize=16)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:12.830707Z","iopub.execute_input":"2024-11-08T18:18:12.831063Z","iopub.status.idle":"2024-11-08T18:18:13.481376Z","shell.execute_reply.started":"2024-11-08T18:18:12.831027Z","shell.execute_reply":"2024-11-08T18:18:13.480292Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"metadata.age = (metadata.age\n .replace({'16month':'1','39yr':'39','1yr':'1','male35':'35','female24':'24'})\n .astype(int))\ncustom_palette = ['darkorchid','tomato']\n\nplt.figure(figsize=(14,15))\nplt.subplot(321)\nplt.hist(metadata.age,color='darkorchid')\nplt.box(False)\n\nplt.subplot(322)\nplt.boxplot(metadata.age,vert=False,showmeans=True)\nplt.box(False)\n\nplt.subplot(323)\nsns.histplot(metadata,x='age',hue='gender',palette=custom_palette)\nplt.box(False)\n\nplt.subplot(324)\nsns.boxplot(metadata,x='age',y='gender',palette=custom_palette)\nplt.box(False)\n\nplt.subplot(325)\nsns.histplot(metadata,x='age',hue='ptb',palette=custom_palette)\nplt.box(False)\n\nplt.subplot(326)\nsns.boxplot(data=metadata, x='gender', y='age', hue='ptb', palette=custom_palette)\nplt.box(False)\n\n\nplt.suptitle('Age Analysis',fontsize=16)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:13.482524Z","iopub.execute_input":"2024-11-08T18:18:13.482840Z","iopub.status.idle":"2024-11-08T18:18:14.731495Z","shell.execute_reply.started":"2024-11-08T18:18:13.482806Z","shell.execute_reply":"2024-11-08T18:18:14.730466Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# FacetGrid of Age by Gender and PTB\nplt.figure(figsize=(14,8))\ng = sns.FacetGrid(metadata, col='ptb', row='gender', margin_titles=True,aspect=1.75)\ng.map(sns.histplot, 'age', bins=10, kde=True)\n\n# Adding titles and labels\ng.set_axis_labels('Age', 'Count')\ng.set_titles(col_template=\"{col_name} PTB\", row_template=\"{row_name} Gender\")\n\n# Show the plot\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:14.732670Z","iopub.execute_input":"2024-11-08T18:18:14.732954Z","iopub.status.idle":"2024-11-08T18:18:16.099335Z","shell.execute_reply.started":"2024-11-08T18:18:14.732923Z","shell.execute_reply":"2024-11-08T18:18:16.098355Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"##

Lungs Mask

","metadata":{}},{"cell_type":"code","source":"def get_colored_mask(image, mask_image,color = [255,20,255]):\n mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY)\n mask = cv2.bitwise_and(mask_image, mask_image, mask=mask_image_gray)\n mask_coord = np.where(mask!=[0,0,0])\n mask[mask_coord[0],mask_coord[1],:]= color\n ret = cv2.addWeighted(image, 0.6, mask, 0.4, 0)\n return ret\n \nfilenames = next(os.walk(IMG_PATH))[2][:3]\nfor file in filenames:\n img = imread(IMG_PATH+file)\n msk = imread(MSK_PATH+file)\n\n plt.figure(figsize=(15,5))\n\n plt.subplot(131)\n plt.imshow(img)\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n \n plt.subplot(132)\n plt.imshow(msk,cmap='binary_r')\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n\n plt.subplot(133)\n plt.imshow(get_colored_mask(img,msk))\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n\n plt.tight_layout()\n plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T19:42:03.233487Z","iopub.execute_input":"2024-11-08T19:42:03.233943Z","iopub.status.idle":"2024-11-08T19:42:15.687405Z","shell.execute_reply.started":"2024-11-08T19:42:03.233900Z","shell.execute_reply":"2024-11-08T19:42:15.686319Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"filenames = next(os.walk(IMG_PATH))[2]\nheatmap = np.zeros((1024,1024))\nfor file in tqdm(filenames,total=len(filenames)):\n msk = imread(MSK_PATH+file,0)\n msk = resize(msk,(1024,1024))\n heatmap += msk\nheatmap /= len(filenames)\n \nplt.figure(figsize=(12,12))\n\nplt.imshow(heatmap,cmap='coolwarm')\nplt.yticks([])\nplt.xticks([])\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:28.236708Z","iopub.execute_input":"2024-11-08T18:18:28.237045Z","iopub.status.idle":"2024-11-08T18:18:59.038172Z","shell.execute_reply.started":"2024-11-08T18:18:28.237011Z","shell.execute_reply":"2024-11-08T18:18:59.037232Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

Custom Data Generator Explanation

\n\nIn deep learning tasks such as semantic segmentation, you often need to feed data into the model in batches during training. This is where a **data generator** comes in handy. A data generator is responsible for dynamically loading and processing images (and their corresponding masks) in batches during model training. This avoids loading the entire dataset into memory, which can be resource-intensive, especially with large datasets.\n\nThe provided code defines a **custom data generator** class that inherits from `keras.utils.PyDataset`. This custom generator is designed for image segmentation tasks, where both **input images** and **masks** need to be loaded, preprocessed, and returned in batches.\n\n##

Key Components

\n\n#### **1. `__init__` Method (Constructor)**\nThe constructor initializes the data generator object, accepting several parameters:\n- **`img_files`** and **`mask_files`**: Lists of image filenames and corresponding mask filenames.\n- **`batch_size`**: The number of image-mask pairs to process in each batch.\n- **`size`**: The target size to which the images and masks will be resized.\n- **`seed`**: A random seed for reproducibility.\n- **`shuffle`**: Whether to shuffle the dataset after each epoch to introduce randomness and prevent overfitting.\n\nThe constructor also verifies that the number of images and masks is the same, which is crucial for segmentation tasks. If `shuffle` is enabled, it initializes the shuffling of the dataset using the `on_epoch_end` method.\n\n#### **2. `__len__` Method**\nThis method returns the number of batches that will be generated per epoch, i.e., the number of times the generator will yield a batch of data. It is calculated by dividing the total number of samples by the batch size:\n```python\nreturn int(np.floor(len(self.img_filenames) / self.batch_size))\n```\n\n#### **3. `__getitem__` Method**\nThis is the main method of the data generator, which generates and returns a single batch of data (images and corresponding masks) given an index:\n- **`index`**: The index of the batch to generate.\n- The method first determines which image-mask pairs to include in the current batch by using the `index` and the `batch_size`.\n- **Preprocessing**:\n - **Image**: Each image is read using `imread`, resized to the specified size, and normalized to the range [0, 1] by dividing by 255.\n - **Mask**: Each mask is read, resized to the specified size, and then normalized similarly to the image. Additionally, all pixel values greater than 0.5 are set to 1, ensuring the mask is binary.\n- **Return**: A batch of images and masks is returned as NumPy arrays.\n\n#### **4. `on_epoch_end` Method**\nThis method is called at the end of each epoch (i.e., after every complete pass over the dataset). It shuffles the dataset indices if `shuffle` is set to `True`, ensuring that the data is presented in a different order for each epoch. This helps prevent the model from learning biases related to the order of the data:\n```python\nif self.shuffle:\n np.random.shuffle(self.indexes)\n```\n\n##

Data Preparation

\n\nBefore using the generator, you must provide a list of image and mask file paths:\n- **`img_files`**: List of filenames of the input images.\n- **`msk_files`**: List of filenames of the corresponding ground truth masks.\n\nThe generator uses the **train-test split** to separate the data into training and validation sets.\n\n##

Generator Usage

\n\n#### **Creating the Data Generator Objects**\nTwo data generators are created:\n1. **`train_data`**: For training data.\n2. **`val_data`**: For validation data.\n\nBoth generators are configured with a batch size of 8, a target image size of (512, 512), and options for multi-threaded data loading using `workers` and `use_multiprocessing`.","metadata":{}},{"cell_type":"code","source":"class DataGenerator(keras.utils.PyDataset):\n def __init__(self, img_files, mask_files, 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.img_filenames = img_files\n self.mask_filenames = mask_files\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.img_filenames) == len(self.mask_filenames), \\\n \"The number of images and masks must be the same\"\n \n self.indexes = np.arange(len(self.img_filenames)) # 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.img_filenames) / 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 masks = []\n \n for i, idx in enumerate(batch_indices):\n # Load and preprocess image\n img = imread(IMG_PATH+self.img_filenames[idx],0) # Read image\n img = resize(img, self.size) # Resize to target size\n img = img / 255.0 # Normalize to [0, 1]\n \n # Load and preprocess mask\n mask = imread(MSK_PATH+self.mask_filenames[idx],0) # Read mask \n mask = resize(mask, self.size) # Resize to target size\n # mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1)\n mask = np.expand_dims(mask, axis=-1) # Add channel dimension\n mask = mask / 255.0 # Normalize to [0, 1]\n mask[mask > 0.5] = 1 # Binary mask\n \n # Add image and mask to the batch arrays\n images.append(img)\n masks.append(mask)\n \n return np.array(images), np.array(masks)\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\nimg_files, msk_files = sorted(os.listdir(IMG_PATH)), sorted(os.listdir(MSK_PATH))\n\ntrain_img_files, val_img_files, train_msk_files, val_msk_files = train_test_split(img_files,msk_files,test_size=0.3,random_state=1)\n\ntrain_data = DataGenerator(\n img_files=train_img_files, \n mask_files=train_msk_files, \n batch_size=8, \n size=(512, 512),\n workers=4, \n use_multiprocessing=True\n)\n\nval_data = DataGenerator(\n img_files=val_img_files, \n mask_files=val_msk_files, \n batch_size=8, \n size=(512, 512),\n workers=4,\n use_multiprocessing=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:18:59.040741Z","iopub.execute_input":"2024-11-08T18:18:59.041076Z","iopub.status.idle":"2024-11-08T18:18:59.124413Z","shell.execute_reply.started":"2024-11-08T18:18:59.041041Z","shell.execute_reply":"2024-11-08T18:18:59.123580Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

U-Net Acritecture Overview

\n![Group14.jpg](attachment:19781c52-2424-42ca-8a50-5583ccdc2fd1.jpg)\n\nU-Net consists of two main paths: the **Contracting Path** and the **Expansive Path**. It follows an encoder-decoder structure.\n\n##

Explanation of Key Components

\n1. **Input Layer**: The model starts with an input layer defined by the shape of the images, which in this case is `(256, 256, 1)` for grayscale images.\n2. **Contracting Path (Encoder)**:\n - The encoder consists of three convolutional blocks, each followed by ReLU activations and max pooling for downsampling.\n - Each block captures features at increasing levels of complexity, starting from basic features with 64 filters to more complex features with 256 filters.\n3. **Bottleneck**:\n - The bottleneck consists of two convolutional layers with 512 filters, which captures the most complex features of the input image.\n4. **Expansive Path (Decoder)**:\n - The decoder also has three blocks that mirror the encoder. Each block consists of upsampling followed by concatenation with the corresponding encoder block to retain spatial information.\n - Each upsampling block reduces the number of filters, starting from 256 down to 64.\n5. **Output Layer**:\n - The final layer outputs the segmentation map with the same width and height as the input image, using a 1x1 convolution and a sigmoid activation function for binary segmentation.\n![unet_model.jpg](attachment:a812c6c1-35ee-445f-9055-98c255ed9e28.jpg)","metadata":{},"attachments":{"19781c52-2424-42ca-8a50-5583ccdc2fd1.jpg":{"image/jpeg":"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z/APAW4/8AjdH/AA2z8Ev+h+s//AW4/wDjdAHuFFeH/wDDbPwS/wCh+s//AAFuP/jdH/DbPwS/6H6z/wDAW4/+N0Ae4UV4f/w2z8Ev+h+s/wDwFuP/AI3R/wANs/BL/ofrP/wFuP8A43QB7hRXh/8Aw2z8Ev8AofrP/wABbj/43R/w2z8Ev+h+s/8AwFuP/jdAHuFFeH/8Ns/BL/ofrP8A8Bbj/wCN0f8ADbPwS/6H6z/8Bbj/AON0Ae4UV4f/AMNs/BL/AKH6z/8AAW4/+N0f8Ns/BL/ofrP/AMBbj/43QB7hRXh//DbPwS/6H6z/APAW4/8AjdH/AA2z8Ev+h+s//AW4/wDjdAHuFFeH/wDDbPwS/wCh+s//AAFuP/jdH/DbPwS/6H6z/wDAW4/+N0Ae4UV4f/w2z8Ev+h+s/wDwFuP/AI3R/wANs/BL/ofrP/wFuP8A43QB7hRXh/8Aw2z8Ev8AofrP/wABbj/43R/w2z8Ev+h+s/8AwFuP/jdAHuFFeH/8Ns/BL/ofrP8A8Bbj/wCN0f8ADbPwS/6H6z/8Bbj/AON0Ae4UV4f/AMNs/BL/AKH6z/8AAW4/+N0f8Ns/BL/ofrP/AMBbj/43QB7hRXh//DbPwS/6H6z/APAW4/8AjdH/AA2z8Ev+h+s//AW4/wDjdAHuFFeH/wDDbPwS/wCh+s//AAFuP/jdH/DbPwS/6H6z/wDAW4/+N0Ae4UV4f/w2z8Ev+h+s/wDwFuP/AI3R/wANs/BL/ofrP/wFuP8A43QB7hRXh/8Aw2z8Ev8AofrP/wABbj/43R/w2z8Ev+h+s/8AwFuP/jdAHuFFeH/8Ns/BL/ofrP8A8Bbj/wCN1498bP8AgoZong7xV4Xm8C3tl4x0KVJxq9sI5IZFIaPyyjsowceZ2I9R0oA+0aK8m+DH7TfgX45WUZ0HVUh1TaDLpN2RHcocc4H8Y91z74r1bccdRQA+ikHSloAKKKKAGt1r8uPjR/ya78Nf+w5rP/o+v1HbrX5cfGj/AJNd+Gv/AGHNZ/8AR9fe8Jf7wv8AHH/0iqfGcS/wf+3Jf+lUz9LvBH/Il6B/2D7f/wBFrW3WD4KYjwZoAH/QPt//AEWtakt/DBLFHJPHHJM22NHYAucZwAevAJ4r4ar/ABJerPr6XwR9Dif2gf8AkhXxE/7F6/8A/Sd6+D/+CcH/ACXnUf8AsAXH/o+3r7v+PzbvgR8RDnP/ABT1/wD+k718H/8ABOLj48al6/2Bcf8Ao+3r7/Jf+RDjv66HxWa/8jnB/wBdT9MF6UGo95zjNR2t5FfQrNbzRzwt9142DKfoRX530PuT8xv+ChH/ACcRP/2C7X/2asT9hb/k53wp/wBcr3/0klra/wCCg/P7Q8//AGC7X+TVi/sL/wDJznhX/rnef+kktfvtP/kmf+4L/wDSWfjE/wDkf/8AcVf+lH6uDqaG6VEXI6Ec1laJ4v0jxJPf2+malbXtxYTvbXUMUgLwSIxVlZeo5Br8EUW05JaI/ZeaKaTerPgj/gph/wAlI8I/9gl//RzV4T+y/wD8nC+AP+wtD/Ovdf8AgpeSfiR4R/7BL/8Ao5q8K/Zf/wCThfAH/YWh/nX71lX/ACTq/wAEv1PxrMf+R4/8cf0P1+vrOLULOe1nQSQzxtG6HupGCK+PvD/7OH7QXwV0O4+Hnwp8deDrf4ZzTXJsbvX7C5fVtDimZn8uDY/lzbWdiGkIOTk56V9g6hexabY3F3O22GCNpHb0UDJ/lXzd8Nf2wdd+Lr6ZrfhP4Sazq3w91C/+ww+JY9StxIMS+W0zWx+YRg9Tu3Y/hxzX4Gj9nOU8WfsN6n4P+Gnwfs/hNrOm23jD4ZXVxd2E/iOKQ2moSXI/0ozCMll3t8w252jgY4NYGnfsT/EzxrefHm5+I3ifw5Ld/EnRrCyiufD8M0a2s0BLFTFID8gO1Q28swBJCk4r6X8Z/tKfDL4deLIPDPiPxhp2la5JtzaSlz5W4Ar5rKpWLIIxvK57VY8dftC/Dn4aalp2n+JfF2naXfX6CS2gdi7NGejkIDtT/abA96YHxN8ffDPxU8O65+y14f1a78L2vjSw8Rm0068sFnnsSEhxG0quFbJA5APGeK9jsP2OfFHxBb4ta/8AE7XtITxZ480P/hH0Xw3BL9l061XJXBlIMpLbWJKjGCMmvVZf2uPgz/wjmleJJvHWlpouoXNxaWWoTJIsck0P+tVWKcEevftmqUf7b3wJbTtMvl+JmifZ9Sm+zWx3vvZ84wybdyDPdgB70AeJfD39kz41r8Ufgp4g8d+JfBl5ovw2t7vSrS00SC5ime0e08iOVnfcHlOyLK4RRgkFia9V/Y3+Bnjn9m3wne/D7V77QtW8EadcTyaDf2bTDUHSWZ5WW5Rl2AguRlCenvx6Z8Rvjr4C+E2lWGo+K/EtnpVrfjNnndM9yMA5jSNWZhggkgEAHJqDWv2h/ht4b8I6J4r1Hxlplv4b1qcWun6qZC1vNIVZtu8AhThGzuxjbjrxQB6PRXiz/tf/AAru/hf4j8e6P4tstc0HQlYXb2hYOJBnCbWAILEYBIwc9a6L4DfHfwx+0N8P7DxZ4XufNtbhB51u5/e2shGTHJjjcPagD0eiiigAooooAKKKKACiiigAooooA4b40trg+E/i1/Dl/DpWtR6ZcSW13PGZFjZY2OcDHOAcehr89YfG3xAk/wCCYg8T+LNcsPFQ1G+02OxW/sxPJHE18qSCYvkSMcjBxxiv08u7OG+tZradBLBMjRyI3IZSMEH8DXg7/sQfDI+Btd8HLDq8fhvV7yG+l05NRfyoJIpRKghH/LNd4BIHWgDw79l3VvifqH7Xnxp0bUfF9pd+FNB+wibSWtm2iGWyZrdbcZxFszHv4+Yqema8j/Zy/aK1T4KfsQ/A7QNCuItL1jxhrOr2i6zcWbXcdhFFdStJJ5Sg73+dAq4OefSvvhv2avBafFeL4i2sV/p/iQQrbzvZ3jRw3iquxPPjHEhUcAnpWJo/7HHwz0T4P6N8NoNNu28P6Ncy3mnTPdt9rtJpHd2eOYYZTl2/CgDnv2TPjr4m+JmpeMfDPid21e58PSQG28Rx6VLYQ6lDKuQwjdQA6kMCF46etbH7Yxz8LNJ/7GPTP/R4r0T4X/CfRfhLos2m6PPqV2s8vnTXGq3j3U0jYAyXb2A6V53+2Px8LdK/7GPTP/R4r1sp/wB/o/4kebmX+51fRnnP7TX7aXiL4F/E+XwxpugaZqVqlpDcia6aQPl85HykDHFfNnj39ub4geMvEuh6zpog8M3OmLKnlWDu8dyHKHEiuSCBs/U1+iXjH4DfD/4haydX8R+FbDV9SZFiNzcKS21eg4PQZrI0z9ln4V6Lrlpq1j4M063urUHywqEpk4+YqSQSMcfU134XG5fQpx5qN5pavv8Aj+h9JSr4eEVeGp89fEP4xeKvi7+xr42vPE3hOfQJI47LZeN8sV3/AKZDyqH5h/LniuH/AOCfeuzeGND+MGswRpLNp+kw3aRyfdZo1uXAPscV9O/tsRJD+y541RFCIqWYVVGAB9sg6V85/wDBNfTbbWJviZYXsS3Fpc2dpDNE/SRG88Mp9iCRX0uDnCfDeLnGNlzrT50z84x8oy4kw7SsuX/5Mym/4KY+MCOfCOiDj/npN/8AFVzvwE/a7+J2neLW0+3sbjxhYXlwz/2Xgu8IZicRv1UDOBnivt4fsm/CEgf8UDpP/fDf/FV1Hw++D/hD4W2jQeGtEttNDks0iLmRs+rHnFfOyx+XRpyjSw+r7/8AD3+4/SXXwyi1Cnufl3+3brd/qv7Xqm7hm08SeELKd9PmfcYXMsmQccZFUP2OPEFzYftqfCnTItv2e/GqmXI5+TTp2XH412X/AAUNvNNP7V72iQ41VfDtpI0u3rGZJABn61yv7GWl2F1+2P8ADK9nn8u+tRqf2eLOPM3afOG+uBX3FCFRcKz9lUV7N77K+sfuurfI/Hq86b4ji6kLK66dbaP77O/zP0G/bdP/ABi148I5Hk2//pVDX481++l3p1rqlpJa3ltFd20mA8M6B0fByMqeDzzWSPh54VH/ADLWj/8AgBF/8TX4+fpp+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUV+7v/CvfC3/QtaP/AOAEX/xNH/CvfC3/AELWj/8AgBF/8TQB+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUV+7v/CvfC3/QtaP/AOAEX/xNH/CvfC3/AELWj/8AgBF/8TQB+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUV+7v/CvfC3/QtaP/AOAEX/xNH/CvfC3/AELWj/8AgBF/8TQB+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUV+7v/CvfC3/QtaP/AOAEX/xNH/CvfC3/AELWj/8AgBF/8TQB+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUV+7v/CvfC3/QtaP/AOAEX/xNH/CvfC3/AELWj/8AgBF/8TQB+EVFfu7/AMK98Lf9C1o//gBF/wDE0f8ACvfC3/QtaP8A+AEX/wATQB+EVFfu7/wr3wt/0LWj/wDgBF/8TR/wr3wt/wBC1o//AIARf/E0AfhFRX7u/wDCvfC3/QtaP/4ARf8AxNH/AAr3wt/0LWj/APgBF/8AE0AfhFRX7u/8K98Lf9C1o/8A4ARf/E0f8K98Lf8AQtaP/wCAEX/xNAH4RUV+7v8Awr3wt/0LWj/+AEX/AMTR/wAK98Lf9C1o/wD4ARf/ABNAH4RUn6V+73/CvfC3/QtaP/4ARf8AxNef+P8A9lv4f/EjxRoOq6votuIdIWUJY2sSwwzs5Q5kCgbgNnA/2jQB+XfwJ+AfxE+Kuu2tx4QsrmxihkBOssWhhh56h+59hX64/Czwvr/g/wAF2Gl+I/EcvifVoFxLqE0YQt7ccnHqea6bSNFsNA0+Gx020hsbOEBY4IECIo+gq5gUAA6UtFFABRRRQA1utflx8aP+TXfhr/2HNZ/9H1+o7da/Lj40f8mu/DX/ALDms/8Ao+vveEv94X+OP/pFU+M4l/g/9uS/9Kpnr37W/i/4keGovhzH4GvtftLWXQo2uV0dZChfCgbtgPOPWvlnxf8AED4t6mdNPiXVfE7+Tch7M3/nLibBA2bgOcZ6V+uvghQfBmgcf8w+3/8ARa1Y1bwxpWvTWUuo2EF89nL51uZ0D+W+CNwB74JFeRQzWGG/duinZvXru/I/QMLi1SpQi4J2R8f+A/FHxi179m74gf8ACfaev9jf8I1fm1v735Lx/wDR3wCvce55r56/Y1ub6y8VfEG40t549Ti8E6nJavbZ8xZh5RQpjnduxjHev0Y/aAQD4E/EMDgDw9f/APpO9fB//BOJQ3x51IHp/YFx/wCj7evp8qrqrk+PqqCXkttj8+zianneEklb/hzgP+Fq/tBEc6z43Ix3S4/wrf8A2X/G3xptvFP2bwWl5q1o8+by2vsm1BJ+Ysx+6eucc1+pwQEVnaJ4a0rwzafZdKsINPtySxS3QKCScknHU181POqcqcoewjr935H6Q8bFxa9mj8xP275LyX45o+oxRQ37aPaGeOBiyK+GyAT1FUf2F/8Ak5vwr6eVef8ApJLW1/wUI4/aHn/7Bdr/AOzVi/sLf8nO+FP+ud7/AOkktfqlPXhp/wDXp/8ApLPwSprn/wD3FX/pRt/Fb4CfGfVvih4xvtM8Pa7Npl1rV5PayRT4RomnYoVG/oVII9qwfgj8IfjHd+P5/wDhGYtR0LUbG5aG+v7hykUcith1c5w5yCMDPSv1d2A5pkdrDbhhFGsYZi7BQACxOST7k96+BjxhiVh3RdGG1tn+KvqfZvhii6yqqrLe/wDwz6H5tf8ABQCz1ew8S+A7fXr6LU9Xj0Ui4uoYvLWRvObJC9q8i/Zf/wCThfAH/YWh/nXuv/BS8Y+JHhH/ALBL/wDo5q8K/Zf/AOThfAH/AGFof51+gZZLn4fUn1hP/wBuPisfHlzvlXSUf0P191AMbC4CQLcv5bYhY4DnH3SffpX53ar8EPHTeIbOf4V/C3xD8HPGMuo28t5qdrrobQ0iE26Y+QDtkypf5dozu5r9FLu5SztZriUkRxIZGIGeAMmuG+C3xx8H/H/wJF4u8Gakb/R5JZIC00ZikjdTgq6Nyp6HnsQa/AEftJ8R/Ej9m/4qaV8TPi3aCz1zxH4b+IOoPcx3mjxWL7IpECiGZ50Z4hHnapU9FzjNaUngTXv2Z/jZquonT9M+JdrqfhLT9O269q9vBeWH2eIxs8gcYKPjJKgZxX2P8Mfjv4M+MGn+I77wxqyXlhoGqy6Ne3Ui+XELiNVZtjNwy4dcMODVHx78J/hR8WUTXfFOjaFr62qhf7RuJFKxqD91nVgMZ7E4pgfn9+z18GvE3j7wF+z3qMXhRr/QrD4garqephFV7eC0eRtrkE8pnpgGvTda/Zj8QyyfthvB4Ey3iS1CeFSsEf8ApLfZSD5HPy/vMenNfc/hj/hHtLsYdF0BtPgtrOMJHZWLpiJMcYVTwK3BzQB+ePi74BfFzQrv4N+ONMtdXvH0bwVBoGo6RYx2s95YThF3uiXCsjFvusRyAgweaNe/ZU8XQ/Bz4UaVBous61JN8VLbxTrGlal9nZtNtHlJnUrGFQR9XKgcGVh2r9ENoo2igD4pn/Z38SXvx7/aYFl4bGneF/Ffhixs9HnMaLaXF0toyvtUdCHPJwOTmvVP2INH8Q+DPgVoPg3xN4NufCep+HLaPT5JZmjKX7KOZkK9j7819BYoCgUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4R+2P/wAkt0r/ALGPTP8A0eK7f4ifHz4f/CbX9D0Xxd4psNB1PW32WFvdFsynIGSQCEXJA3MQCeM5ryb9pHx9ovxE+DdlqGhXRvLS38X2VjJJ5bJiaK52uBuxkA9xwe2a9bKf9/o/4kebmX+51fRn0wOlLSL0FcN8WPjf4I+B2jwan438QW+hWtxJ5UAdJJpp3xnbHFGrO5wCflU4ryT0jhf23P8Ak1/xv/u2n/pZBXz1/wAExP8AkL/EL/rhY/8AoU9exftO/ETw58U/2OPFniLwprFrrmi3S2oiu7ViVJW9hDKQcFWBGCpAIPUV47/wTE/5C3xC/wCuFj/6FPX6HgP+SWxf+NfnTPhsZ/yUWG/wv/28++B0paZuIwK8ck/bG+DMXjMeFH+IGlrr/wBu/s77GRJkXH9wts2gZ43E7c8ZzxX54fcnwP8A8FC9Cuo/2xJtXKr9jk8MWkAIYZ3CWUnjr0rif2QrWaX9ub4NzJGzwxLrG9wOFzps4GT2ya67/goTqt3J+2bc6eZ2+xJ4Ws5RCegYyyDdWF+xr4jl079s74WaUkSNDqA1Qs5PKeXp07DH1r9apLDy4UmrtLr195SX4N2Xkj82qOvHiSLsm/u92z/FI/YFPu06moeDTq/JT9JCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKaUBOadRQAUUUUAFFFFABVHWtasfD2l3epaneQafp9pE0091cyBI4kUZZmY8AD1q9Xin7ZOmaZrP7OHjGy1WS5W3mW0EaWkKzyT3Au4TbweWzoriSYRxlWZVIdgSBQB6DovxO8K+Jb3S7XSfEGn6lNqlpJf2K2twsn2iBGVXkTB5UM6An3FfnB8Z/+TXPhpn/oN6x/6Or0z9kcaJ4l/alv/ENr4W1L4XXVvpOp2KeD5p4LiwmmS9jhvJrUwviFkltgskW3aS6upIbJ8y+M/H7Lnw0Hpresf+jq+94T/wB4X+OP/pFU+M4l/gv/AAy/9Kpn6X+CP+RL0D/sH2//AKLWjxl4y0b4f+G77X/EGoQ6XpFlH5s91OcKi/4+1Hgj/kS9A/7B9v8A+i1rzT9rmHSrn4F65FqkeozM0tuLCPSTH9qa981fswj8w7M+bs+/8vrxXw9X+JL1Z9fS+CPojnfE37Q3gT44fA34pR+Eda+3XNj4dvmuLSe3kt50QwSAP5ciq20kcNjBr5X/AOCcP/JetRx/0ALj/wBH29aPwS8Yaz478JfGfV/iRd61cfEmPwbqtjanUobOGA2EMkkc6xLbMwLpOiK5fuVK/K2azv8AgnDn/hfWpZ/6AFxx/wBt7evv8l/5EOO/rofFZr/yOcH/AF1P0t3YFeNeNf2wvhJ8P/Flz4a13xfa2et20qQzWYR5GjZvXaDwMjJ6DvivZDg+hNfln+0QuqP+0P4mPhaPxV9gF7feaYJNMFsYwF/tXZ5p83O3pkf7tfnR9yix+37dRXvx+a4gkWaCbSLSSOROQykMQQe4INZX7C3/ACc74U/65Xv/AKSS039s02LfFHRjpgkGmnw3ppthKfn8ryvk3e+3FO/YW/5Od8Kf9cr3/wBJJa/faf8AyTX/AHBf/pLPxif/ACP1/wBfV/6Ufq0xIzj9a+JPFX/BQ+Lwx4sv9CbR9Rvmsp5Ipru10x2gVImCTyb84KxsQGI6E19syHBH1r8ZfjjoWmRftBeK7ePVNE04W2oX0s1hdeJZra5njgnRJ7c2wBCvdswkRQfnC1/Kee4PHYupSWElOMUp35JKOvu8t7tX627a36H73gatClGXtUm9LXV+9/0Pdf8AgoBrLeIvEHw91NirG70ETZQYB3SseleQ/sv/APJwvg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unet(input_shape, num_classes):\n # Input layer for the model\n inputs = layers.Input(shape=input_shape)\n \n # Contracting Path (Encoder)\n # First block with 64 filters\n conv1 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)\n conv1 = layers.BatchNormalization()(conv1) # Add Batch Normalization\n conv1 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)\n conv1 = layers.BatchNormalization()(conv1) # Add Batch Normalization\n pool1 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv1) # Downsample\n\n # Second block with 128 filters\n conv2 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)\n conv2 = layers.BatchNormalization()(conv2) # Add Batch Normalization\n conv2 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)\n conv2 = layers.BatchNormalization()(conv2) # Add Batch Normalization\n pool2 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv2) # Downsample\n\n # Third block with 256 filters\n conv3 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)\n conv3 = layers.BatchNormalization()(conv3) # Add Batch Normalization\n conv3 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)\n conv3 = layers.BatchNormalization()(conv3) # Add Batch Normalization\n pool3 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv3) # Downsample\n\n # Bottleneck layer with 512 filters\n conv4 = layers.Conv2D(512, (3, 3), activation='relu', padding='same')(pool3)\n conv4 = layers.BatchNormalization()(conv4) # Add Batch Normalization\n conv4 = layers.Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)\n conv4 = layers.BatchNormalization()(conv4) # Add Batch Normalization\n\n # Expansive Path (Decoder)\n # First upsampling block\n up6 = layers.Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv4) # Upsample\n merge6 = layers.concatenate([up6, conv3]) # Concatenate with the corresponding encoder block\n conv6 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(merge6)\n conv6 = layers.BatchNormalization()(conv6) # Add Batch Normalization\n conv6 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)\n conv6 = layers.BatchNormalization()(conv6) # Add Batch Normalization\n\n # Second upsampling block\n up7 = layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6) # Upsample\n merge7 = layers.concatenate([up7, conv2]) # Concatenate with the corresponding encoder block\n conv7 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(merge7)\n conv7 = layers.BatchNormalization()(conv7) # Add Batch Normalization\n conv7 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)\n conv7 = layers.BatchNormalization()(conv7) # Add Batch Normalization\n\n # Third upsampling block\n up8 = layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7) # Upsample\n merge8 = layers.concatenate([up8, conv1]) # Concatenate with the corresponding encoder block\n conv8 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(merge8)\n conv8 = layers.BatchNormalization()(conv8) # Add Batch Normalization\n conv8 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)\n conv8 = layers.BatchNormalization()(conv8) # Add Batch Normalization\n\n # Output layer for segmentation\n outputs = layers.Conv2D(num_classes, (1, 1), activation='sigmoid')(conv8)\n\n # Create the model\n model = models.Model(inputs=[inputs], outputs=[outputs])\n return model # Return the model\n\n# Instantiate the model\ninput_shape = (512, 512, 1) # Define the input shape for grayscale images\nmodel = unet(input_shape, 1) # Create U-Net model for binary segmentation\n\n# Print the model summary\nmodel.summary() # Display model architecture\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:20:19.702236Z","iopub.execute_input":"2024-11-08T18:20:19.702941Z","iopub.status.idle":"2024-11-08T18:20:20.015293Z","shell.execute_reply.started":"2024-11-08T18:20:19.702903Z","shell.execute_reply":"2024-11-08T18:20:20.014385Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

Loss & Metric Function

\n### **Binary Cross-Entropy Loss**\n\n**Binary Cross-Entropy (BCE)**, also known as **log loss**, is a loss function commonly used for binary classification tasks, including binary segmentation in image processing. It measures the difference between the predicted probabilities and the actual labels.\n\n### **Formula**\n\nThe Binary Cross-Entropy loss for a single sample is given by:\n\n$$\n\\text{BCE} = - \\left[ y \\log(p) + (1 - y) \\log(1 - p) \\right]\n$$\n\nWhere:\n- $ y $ is the actual label (0 or 1) for the given sample (binary class).\n- $ p $ is the predicted probability that the sample belongs to class 1 (the \"positive\" class).\n- $ \\log $ is the natural logarithm.\n\nFor a set of samples, the total loss is the **average BCE** across all samples in the batch:\n\n$$\n\\text{Total BCE} = \\frac{1}{N} \\sum_{i=1}^{N} \\left[ y_i \\log(p_i) + (1 - y_i) \\log(1 - p_i) \\right]\n$$\n\nWhere:\n- $ N $ is the total number of samples in the batch.\n- $ y_i $ is the actual label for the $ i $-th sample.\n- $ p_i $ is the predicted probability for the $ i $-th sample.\n\n- **When the true label is 1**: The first part of the formula $ y \\log(p) $ dominates. If the predicted probability $ p $ is close to 1, the loss will be small. If $ p $ is closer to 0, the loss will be large (i.e., high penalty for wrong predictions).\n\n- **When the true label is 0**: The second part of the formula $ (1 - y) \\log(1 - p) $ dominates. If the predicted probability $ p $ is close to 0, the loss will be small. If $ p $ is close to 1, the loss will be large.\n\n\n\n\n\n\n### **Jaccard Index (IoU)**\n\nThe **Jaccard Index**, also known as the **Intersection over Union (IoU)**, is a metric commonly used to evaluate the performance of segmentation models. It measures the overlap between the predicted segmentation mask and the ground truth mask. The formula for the Jaccard Index is:\n\n$$\nIoU = \\frac{|A \\cap B|}{|A \\cup B|}\n$$\n\nWhere:\n- $ A $ is the set of pixels in the ground truth mask (`y_true`).\n- $ B $ is the set of pixels in the predicted mask (`y_pred`).\n- $ |A \\cap B| $ is the number of pixels where both the ground truth and the prediction are `1` (i.e., the intersection).\n- $ |A \\cup B| $ is the total number of pixels where either the ground truth or the prediction is `1` (i.e., the union).\n\nThe **Jaccard Index** is a number between 0 and 1, where:\n- **0** means no overlap (i.e., poor prediction).\n- **1** means perfect overlap (i.e., excellent prediction).\n\nIn segmentation tasks, a higher Jaccard Index implies that the model is more accurate in segmenting the relevant objects.\n\n### **`dice_coefficient` Function:**\nThe `dice_coefficient` function calculates the Dice similarity coefficient (also known as the Dice score), which is a measure of overlap between two binary masks. It gives a value between 0 and 1, where 1 indicates perfect overlap, and 0 indicates no overlap.\n\n- **Flattening and casting**: Similar to `dice_loss`, both the true and predicted masks are flattened. Additionally, both are cast to `float32` to avoid issues with type compatibility during arithmetic operations.\n- **Intersection**: The intersection is calculated as the element-wise product of the flattened true and predicted masks, and then summed.\n- **Coefficient calculation**: The Dice coefficient is computed as:\n $$\n \\text{Dice Coefficient} = \\frac{2 \\times \\text{Intersection} + \\text{smooth}}{\\text{Sum of True Labels} + \\text{Sum of Predicted Labels} + \\text{smooth}}\n $$\n A higher Dice coefficient means better model performance in terms of segmentation accuracy.\n \n- **Purpose**: The Dice coefficient is a useful metric to evaluate the performance of a segmentation model. The higher the coefficient, the better the overlap between the predicted mask and the ground truth mask.","metadata":{}},{"cell_type":"code","source":"# Jaccard Index Metric\ndef jaccard_index(y_true, y_pred, smooth=100):\n \"\"\"Calculates the Jaccard index (IoU), useful for evaluating the model's performance.\"\"\"\n y_true_f = tf.reshape(tf.cast(y_true, tf.float32), [-1]) # Flatten and cast ground truth\n y_pred_f = tf.reshape(tf.cast(y_pred, tf.float32), [-1]) # Flatten and cast predictions\n intersection = tf.reduce_sum(y_true_f * y_pred_f) # Compute intersection\n total = tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) - intersection # Total pixels\n return (intersection + smooth) / (total + smooth)\n\n\ndef dice_coefficient(y_true, y_pred, smooth=1):\n # Flatten and cast true and predicted masks to float32\n y_true_f = tf.reshape(tf.cast(y_true, tf.float32), [-1]) # Flatten and cast y_true to float32\n y_pred_f = tf.reshape(tf.cast(y_pred, tf.float32), [-1]) # Flatten and cast y_pred to float32\n \n # Calculate the intersection between the true and predicted masks\n intersection = tf.reduce_sum(y_true_f * y_pred_f)\n \n # Calculate the Dice coefficient using the formula\n return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n\n\n# Using the metrics in model compilation\nmodel.compile(optimizer='adam', \n loss='binary_crossentropy', \n metrics=['accuracy',dice_coefficient,jaccard_index])","metadata":{"execution":{"iopub.status.busy":"2024-11-08T18:21:18.750120Z","iopub.execute_input":"2024-11-08T18:21:18.750595Z","iopub.status.idle":"2024-11-08T18:21:18.770683Z","shell.execute_reply.started":"2024-11-08T18:21:18.750551Z","shell.execute_reply":"2024-11-08T18:21:18.769662Z"},"trusted":true},"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\ncheckpoint = ModelCheckpoint(\n 'best_model.keras', # Path to save the model\n monitor='val_dice_coefficient', # 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# Fit the model with multi-processing enabled\nhistory = model.fit(\n train_data, # The training data generator or dataset\n validation_data=val_data, # Validation data ratio \n epochs=128, # Number of epochs\n callbacks=[checkpoint],# Callbacks for checkpoint and early stopping\n)","metadata":{"execution":{"iopub.status.busy":"2024-11-08T18:21:33.485701Z","iopub.execute_input":"2024-11-08T18:21:33.486089Z","iopub.status.idle":"2024-11-08T18:32:54.347064Z","shell.execute_reply.started":"2024-11-08T18:21:33.486055Z","shell.execute_reply":"2024-11-08T18:32:54.345914Z"},"trusted":true},"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')\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')\n\nplt.subplot(223)\nplt.plot(history.history['dice_coefficient'], label='Train Dice Coefficient')\nplt.plot(history.history['val_dice_coefficient'], label='Validation Dice Coefficient')\nplt.title('Model Dice Coefficient')\nplt.ylabel('Metric Value')\nplt.xlabel('Epoch')\n\nplt.subplot(224)\nplt.plot(history.history['jaccard_index'], label='Train Jaccard Index')\nplt.plot(history.history['val_jaccard_index'], label='Validation Jaccard Index')\nplt.title('Model Jaccard Index')\nplt.ylabel('Metric Value')\nplt.xlabel('Epoch')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T18:32:54.354919Z","iopub.execute_input":"2024-11-08T18:32:54.355272Z","iopub.status.idle":"2024-11-08T18:32:55.235165Z","shell.execute_reply.started":"2024-11-08T18:32:54.355230Z","shell.execute_reply":"2024-11-08T18:32:55.234259Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"imgs, msks = val_data.__getitem__(1)\n\nfor img,msk in zip(imgs,msks):\n img = np.expand_dims(img, axis=0)\n pred = (np.squeeze(model.predict(img,verbose=0))*255).astype(np.uint8)\n img = (np.squeeze(img) * 255).astype(np.uint8)\n msk = (msk*255).astype(np.uint8)\n\n # Convert grayscale image to RGB\n img= cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n msk = cv2.cvtColor(msk, cv2.COLOR_GRAY2RGB)\n pred = cv2.cvtColor(pred, cv2.COLOR_GRAY2RGB)\n \n plt.figure(figsize=(12,4))\n\n plt.subplot(131)\n plt.imshow(img)\n plt.title('Image')\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n \n plt.subplot(132)\n plt.imshow(get_colored_mask(img,msk))\n plt.title('Mask (Actual)')\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n\n plt.subplot(133)\n plt.imshow(get_colored_mask(img,pred,color = [255,30,0]))\n plt.title('Mask (Prediction)')\n plt.yticks([])\n plt.xticks([])\n plt.box(False)\n\n plt.tight_layout()\n plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-08T19:18:49.262422Z","iopub.execute_input":"2024-11-08T19:18:49.262819Z","iopub.status.idle":"2024-11-08T19:18:54.919154Z","shell.execute_reply.started":"2024-11-08T19:18:49.262783Z","shell.execute_reply":"2024-11-08T19:18:54.918220Z"}},"outputs":[],"execution_count":null}]}