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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* |