Update README.md

README.md

@@ -1,12 +1,82 @@
 ---
-title: Unet Adam Diceloss
-emoji: ⚡
+title: Polyp Detection AI
+emoji: 🏥
 colorFrom: blue
-colorTo: purple
+colorTo: red
 sdk: gradio
-sdk_version: 5.34.2
+sdk_version: 4.15.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏥 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*