{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-30T14:59:07.778958Z","iopub.execute_input":"2024-05-30T14:59:07.779414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import layers\n# Model architecture (example using pre-trained ResNet50)\ndef create_model(input_shape):\n  base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)\n  base_model.trainable = False\n  x = base_model.output\n  x = layers.GlobalAveragePooling2D()(x)\n  outputs = layers.Dense(num ,classes, activation='softmax')(x)\n  model = tf.keras.Model(inputs=base_model.input, outputs=outputs)\n  return model\n# Compile the model\nmodel = (\"input_shape\")\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n# Train the model (replace with your data)\nmodel.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))\n# Grad-CAM implementation (example function)\ndef grad_cam(model, image, class_index):\n  # ... (implementation details for Grad-CAM using TensorFlow libraries)\n  return cam_output\n# Apply Grad-CAM to analyze a specific MRI scan\ngrad_cam_output = grad_cam(model, test_image, predicted_class)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import layers\n# Model architecture (example using pre-trained ResNet50)\ndef create_model(input_shape):\n  base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)\n  base_model.trainable = False\n  x = base_model.output\n  x = layers.GlobalAveragePooling2D()(x)\n  outputs = layers.Dense(num, activation='softmax')(x)\n  model = tf.keras.Model(inputs=base_model.input, outputs=outputs)\n  return model\n# Compile the model\nmodel = (\"input_shape\")\ncompile('loss='categorical_crossentropy', optimizer= ('metrics=accuracy')\n# Train the model (replace with your data)\nmodel.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))\n# Grad-CAM implementation (example function)\ndef grad_cam(model, image, class_index):\n  # ... (implementation details for Grad-CAM using TensorFlow libraries)\n  return cam_output\n# Apply Grad-CAM to analyze a specific MRI scan\ngrad_cam_output = grad_cam(model, test_image, predicted_class)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}