This project utilizes the EfficientNet model for automated diagnosis of pneumonia from chest X-ray images. It is a fine-tuned version of the pre-trained EfficientNet model adapted for binary classification to differentiate between normal and pneumonia cases.
- Utilizes the EfficientNet model, known for its efficiency and accuracy in image classification.
- Implements image data augmentation to enhance model generalization.
- Includes detailed preprocessing steps for dataset preparation.
- Provides performance evaluation metrics such as accuracy, precision, recall, F1-score, and AUC.
- Clone the repository.
- Install TensorFlow and other required libraries listed in
requirements.txt. - Prepare the dataset, following the preprocessing steps outlined in the code.
The project uses chest X-ray images from publicly available datasets. These images are processed and labeled into two classes: NORMAL and PNEUMONIA.
- Train the model using the preprocessed dataset with image augmentation to improve robustness.
- Evaluate the model using accuracy, precision, recall, F1-score, and ROC-AUC metrics.
- Visualize results with confusion matrices, precision-recall curves, and ROC curves.
Contributions to improve the model and its implementation are welcome. Please fork the repository, make your changes, and submit a pull request.
The project is licensed under the MIT License - see the LICENSE file for more details.
- Creators of the EfficientNet model for their contributions to the field of deep learning.
- Publicly available chest X-ray datasets that facilitate medical imaging research.
For more information and to view the source code, visit the GitHub repository.