1. Run the following command line in your terminal:

   pip install tensorflow numpy matplotlib opencv-python pillow scikit-learn imbalanced-learn
   

2. Download the dataset (link to the dataset: https://www.kaggle.com/datasets/vuppalaadithyasairam/bone-fracture-detection-using-xrays?rvi=1)

3. Copy the path of the dataset folder and paste it into the code

4. After running all the cells, it will create an additional file called best_model.keras (this file stores the model)

5. Enter the path of the image you want in the last cell to check if it has the presence of fracture or not

The code is used for building and training a bone fracture detection model using deep learning techniques, specifically using a pre-trained EfficientNetB3 model. Here's an overview of the steps and functionalities in the code:

1. Import Libraries: The code imports several libraries for image processing, data handling, deep learning, and visualization, such as NumPy, Matplotlib, OpenCV, Seaborn, TensorFlow, and scikit-learn.

2. Dataset Download and Unzipping

   • The dataset (bone-fracture-detection-using-xrays.zip) is downloaded from Kaggle using the kaggle API.
   • The dataset is unzipped and the file contents are extracted for further use.

3. Data Preprocessing

   • ImageDataGenerator is used to apply real-time data augmentation to the images, such as rotation, zoom, and horizontal flipping. These techniques help in preventing overfitting and improving generalization.
   • The data is split into training and validation sets, with the train_path and test_path variables pointing to the respective directories containing the images.
   • The flow_from_directory method is used to load images from the directories for both training and validation, resizing the images to 224x224 pixels and batching them.

4. Model Building

   • EfficientNetB3, a pre-trained model from the Keras applications module, is used as the base model. The weights are loaded from ImageNet, but the top layers are excluded.
   • The base model layers are frozen to prevent retraining, and additional custom layers are added on top:
     • A GaussianNoise layer for regularization.
     • A GlobalAveragePooling2D layer to reduce the spatial dimensions.
     • A dense fully-connected layer with 512 units, followed by batch normalization and another GaussianNoise layer.
     • A Dropout layer for regularization.
     • A final output layer with a sigmoid activation function to classify the images as either "fractured" or "not fractured" (binary classification).
   • The model summary is displayed to show the architecture and number of parameters.

5. Model Compilation: The model is compiled using the binary cross-entropy loss function, Adam optimizer, and metrics like accuracy, precision, recall, and AUC (Area Under the Curve).

6. Model Training

   • The model is trained for 10 epochs using the training data (train_generator) and validation data (validation_generator).
   • ModelCheckpoint saves the best model based on validation accuracy.
   • ReduceLROnPlateau reduces the learning rate if the validation loss plateaus.
   • The training process is carried out with feedback provided via metrics like AUC, precision, recall, and loss values.

7. Model Evaluation

   • After training, the model is evaluated using the accuracy_score and classification_report from scikit-learn to assess the performance on the validation dataset.
   • The performance is evaluated in terms of precision, recall, F1-score, and accuracy.

8. Visualization: Accuracy and Loss plots are generated to visualize the model's performance over the epochs for both training and validation sets. This helps in understanding whether the model is overfitting or underfitting.

9. Model Prediction

   • A function predict_bone_fracture is defined to predict whether an X-ray image shows a fracture or not.
   • The image is preprocessed (resized and converted into an array), and the model is used to make a prediction.
   • The model outputs a confidence score, which is then displayed along with the predicted label ("Fracture" or "Normal").
   • The predicted label and confidence are visualized on the X-ray image itself using Matplotlib.

10. Example Prediction: The function is called twice to predict bone fractures in two different images. One image contains a fractured bone, and the other contains a normal bone X-ray.

• The code uses a transfer learning approach by utilizing a pre-trained EfficientNetB3 model as the base model.
• Data augmentation techniques are applied to the training images to improve model generalization.
• The final model is evaluated using a variety of metrics, and its performance is visualized over epochs.
• Predictions on new images are made and displayed with confidence scores.

• You can further fine-tune the model by unfreezing some of the layers of the EfficientNetB3 model.
• Hyperparameters like learning rate, batch size, and the number of epochs can be tuned for better performance.
• Additional performance metrics such as the confusion matrix and ROC curves could be visualized for more insights.