Pneumonia Detection from Chest X-Ray Images 🫁

📋 Overview

This project implements a deep learning model to detect pneumonia from chest X-ray images using transfer learning with the Xception architecture. The model achieves high accuracy in distinguishing between normal chest X-rays and those showing signs of pneumonia. Chest X-Ray Images (Pneumonia) Dataset

🔄 Processing Flow

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flowchart TD
    A[Input X-Ray Image] --> B[Xception Base Model]
    B --> C[Custom Classification Layers]
    C --> D[Prediction Normal/Pneumonia]
    
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#dfd,stroke:#333,stroke-width:2px
    style D fill:#fdd,stroke:#333,stroke-width:2px

🚀 Features

• Transfer learning using pre-trained Xception model
• Data augmentation and preprocessing
• Binary classification (Normal vs Pneumonia)
• Visualization tools for model predictions
• Detailed performance metrics
• Interactive prediction interface

📊 Model Architecture

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flowchart TD
    A[Input Layer<br>256x256x3] --> B[Xception Base Model<br>Pre-trained]
    B --> C[Batch Normalization]
    C --> D[Dropout 0.45]
    D --> E[Dense Layer<br>220 units, ReLU]
    E --> F[Dropout 0.25]
    F --> G[Dense Layer<br>60 units, ReLU]
    G --> H[Output Layer<br>1 unit, Sigmoid]
    
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#dfd,stroke:#333,stroke-width:2px
    style D fill:#fdb,stroke:#333,stroke-width:2px
    style E fill:#dfd,stroke:#333,stroke-width:2px
    style F fill:#fdb,stroke:#333,stroke-width:2px
    style G fill:#dfd,stroke:#333,stroke-width:2px
    style H fill:#fdd,stroke:#333,stroke-width:2px

📈 Performance

The model achieves:

• Training Accuracy: ~95%
• Validation Accuracy: ~92%
• Test Accuracy: ~90%

🛠️ Installation

# Clone the repository
git clone https://github.com/AmirrHussain/chest-x-ray-images.git

# Install dependencies
pip install -r requirements.txt

📦 Requirements

• Python 3.8+
• TensorFlow 2.0+
• OpenCV
• NumPy
• Pandas
• Matplotlib
• Seaborn

💻 Usage

# Load and preprocess image
image = cv2.imread('path_to_xray.jpg')
image = cv2.resize(image, (256, 256))
image = image / 255.0

# Make prediction
prediction = model.predict(np.expand_dims(image, axis=0))

📁 Dataset Structure

chest_xray/
│
├── train/
│   ├── NORMAL/
│   └── PNEUMONIA/
│
├── test/
│   ├── NORMAL/
│   └── PNEUMONIA/
│
└── val/
    ├── NORMAL/
    └── PNEUMONIA/

📊 Sample Results

Here's how predictions look on test images:

Normal X-Ray → Predicted: Normal (99.2% confidence)
Pneumonia X-Ray → Predicted: Pneumonia (97.8% confidence)

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Dataset provided by Kaggle
• Inspired by various research papers on medical image analysis
• Thanks to the medical community for providing labeled data

📧 Contact

Project Link