---
title: Brain Tumor Classifier
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
---

# 🧠 Brain Tumor Classification AI

**AI-powered MRI analysis for brain tumor detection and classification**

Using MoCo Self-Supervised Learning with Swin-Transformer backbone for high-accuracy tumor classification.

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## ✨ Features

- 🎯 **High Accuracy**: ~95% on test dataset
- 🧠 **4-Class Classification**: Glioma, Meningioma, No Tumor, Pituitary
- 📊 **GradCAM Visualization**: See which regions influenced the prediction
- 📄 **PDF Reports**: Download professional analysis reports
- 📱 **Responsive Design**: Works on desktop, tablet, and mobile

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## 🚀 Quick Start

1. **Upload MRI Image**
   - Click upload area or drag & drop
   - Formats: JPEG, PNG, BMP, TIFF
   - Max size: 16 MB

2. **View Results**
   - Classification with confidence score
   - Probability distribution for all classes
   - GradCAM visualization showing influential regions

3. **Download Report**
   - Enter your name
   - Get professional PDF report
   - Includes all visualizations

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## 🔬 Model Details

**Architecture:**
- Backbone: Swin-Transformer (ImageNet pre-trained)
- Pre-training: MoCo (Momentum Contrast) - 30 epochs
- Fine-tuning: 2-stage process (30 epochs total)
- Head: Linear classifier (192 → 4 classes)

**Training:**
- Dataset: 17,784 MRI images
- Classes: 4 (balanced)
- Validation: 10% held-out
- Test: 10% locked evaluation

**Performance:**
- Accuracy: ~95%
- Precision: ~95% (weighted)
- Recall: ~95% (weighted)
- F1-Score: ~95% (weighted)

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## 📊 Class Information

| Class | Description |
|-------|-------------|
| **Glioma** | Aggressive tumor from glial cells |
| **Meningioma** | Tumor from brain membranes |
| **No Tumor** | Healthy brain with no abnormalities |
| **Pituitary** | Tumor in pituitary gland |

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## 🎓 About GradCAM

The GradCAM (Gradient-weighted Class Activation Map) visualization shows:
- Which regions of the MRI influenced the model's decision
- Warmer colors (red/yellow) = higher importance
- Cooler colors (blue) = lower importance

This helps build trust in AI predictions by showing what the model "looked at" when making its decision.

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## ⚙️ Technical Stack

- **Framework**: Flask
- **Deep Learning**: PyTorch
- **Model**: Swin-Transformer
- **Visualization**: GradCAM
- **Reports**: ReportLab
- **Frontend**: HTML5, CSS3, JavaScript

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## ⏱️ Performance

| Operation | Time |
|-----------|------|
| Model Load | ~10-15s (first time) |
| Classification | ~2-3s |
| GradCAM | Included in classification |
| PDF Report | ~2-3s |
| **Total** | **~15-20s per request** |

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## 🛡️ Data Privacy

- ✅ Images are NOT stored
- ✅ Processed in memory only
- ✅ Temporary files cleaned up
- ✅ No data collection
- ✅ Local processing (no external API calls)

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## ⚠️ Disclaimer

**This tool is for research and educational purposes only.**

This AI model is NOT a substitute for professional medical diagnosis. All results should be verified by qualified healthcare professionals. Always consult a radiologist or medical doctor for clinical interpretation of MRI scans.

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## 📚 Learn More

- **Paper**: MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
- **Swin-Transformer**: Shifted Windows Transformer for CV

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## 🤝 Contributing

Feedback and suggestions welcome!

For issues or improvements:
1. Note the problem
2. Share details about the image/results
3. Report via GitHub Issues

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## 📄 License

Academic use - Research and educational purposes

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**Created with ❤️ for AI in Medical Imaging**

*Last Updated: 2026-04-20*