https://github.com/mjahmadee/automated_diagnosis_of_pneumonia_from_classification_of-chest_xray_images_using_efficientnet

Automated Diagnosis of Pneumonia from Classification of Chest X-Ray Images using EfficientNet
https://github.com/mjahmadee/automated_diagnosis_of_pneumonia_from_classification_of-chest_xray_images_using_efficientnet

efficientnet efficientnetv2 image-classification pneumonia pneumonia-classification pneumonia-detection pneumonia-detector pneumoniac-xray

Last synced: 8 months ago
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Automated Diagnosis of Pneumonia from Classification of Chest X-Ray Images using EfficientNet

• Host: GitHub
• URL: https://github.com/mjahmadee/automated_diagnosis_of_pneumonia_from_classification_of-chest_xray_images_using_efficientnet
• Owner: MJAHMADEE
• License: mit
• Created: 2023-07-10T09:44:30.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-16T13:00:05.000Z (over 1 year ago)
• Last Synced: 2025-01-11T08:51:40.087Z (10 months ago)
• Topics: efficientnet, efficientnetv2, image-classification, pneumonia, pneumonia-classification, pneumonia-detection, pneumonia-detector, pneumoniac-xray
• Language: Jupyter Notebook
• Homepage:
• Size: 3.46 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Automated Diagnosis of Pneumonia from Chest X-Ray Images using EfficientNet 🏥📸

![Python](https://img.shields.io/badge/Python-3.x-blue.svg)

![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)

![Deep Learning](https://img.shields.io/badge/Deep%20Learning-EfficientNet-green.svg)

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.

## Features 🌟

- 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.

## Setup and Installation 🛠️

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.

## Data 📁

The project uses chest X-ray images from publicly available datasets. These images are processed and labeled into two classes: NORMAL and PNEUMONIA.

## Model Training and Evaluation 🚀

- 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.

## Contributing 🤝

Contributions to improve the model and its implementation are welcome. Please fork the repository, make your changes, and submit a pull request.

## License 📜

The project is licensed under the MIT License - see the LICENSE file for more details.

## Acknowledgements 🙌

- 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](https://github.com/MJAHMADEE/Automated_Diagnosis_of_Pneumonia_from_Classification_of-Chest_XRay_Images_using_EfficientNet/).