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| 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](https://huggingface.co/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* |