README.md

metadata

title: Polyp Detection AI
emoji: 🏥
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
license: mit

🏥 AI-Powered Polyp Detection System

An intelligent medical imaging system that uses deep learning to detect colorectal polyps in colonoscopy images.

🎯 Features

• Real-time polyp detection using U-Net deep learning architecture
• Visual segmentation with overlay highlighting detected regions
• Quantitative analysis providing polyp coverage percentages
• Medical-grade interface designed for healthcare applications
• Adjustable sensitivity with detection threshold controls

🔬 Model Details

• Model Repository: ibrahim313/unet-adam-diceloss
• Architecture: U-Net with 32 base channels
• Training Dataset: Kvasir-SEG (1000 polyp images)
• Framework: PyTorch
• Input Size: 384×384 pixels
• Output: Binary segmentation mask

📊 Performance

The model achieves excellent performance on the Kvasir-SEG dataset:

• High sensitivity for polyp detection
• Clinically relevant segmentation accuracy
• Robust performance across various image qualities

🚀 Usage

1. Upload a colonoscopy image
2. Adjust detection threshold if needed (0.1 - 0.9)
3. Click "🔍 Analyze for Polyps"
4. Review the results and segmentation overlay

🔧 Technical Implementation

• Deep Learning: U-Net encoder-decoder architecture
• Preprocessing: Albumentations (resize, normalize)
• Inference: PyTorch with CPU optimization
• Interface: Gradio for user-friendly interaction
• Deployment: Hugging Face Spaces

⚠️ Medical Disclaimer

This AI system is intended for research and educational purposes only. It should not be used as a substitute for professional medical diagnosis. Always consult qualified healthcare professionals for clinical decisions.

📝 Model Information

The underlying model was trained using:

• Loss Function: Dice Loss
• Optimizer: Adam
• Training Epochs: 100
• Validation Strategy: Train/Validation/Test split

🤝 Contributing

This project is open for improvements and contributions. Feel free to:

• Report issues or bugs
• Suggest enhancements
• Share feedback on medical accuracy
• Contribute to model improvements

📞 Contact

For questions or medical AI collaboration opportunities, please reach out through Hugging Face.

---

Built with ❤️ for advancing medical AI research