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.

1. Clone the repository.
2. Install TensorFlow and other required libraries listed in requirements.txt.
3. 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.