---
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.

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*Built with ❤️ for advancing medical AI research*