Deep Learning system for brain MRI classification and segmentation using CT Scan and X-Ray images to diagnose multiple diseases.

• Brain MRI Classification: ResNet18-based classifier for 4 brain conditions
• Segmentation Masks: UNet XL model for generating attention masks
• Real-time Prediction: Fast inference on medical images
• Visualization: Side-by-side comparison of original, prediction, and mask

• Glioma
• Meningioma
• No Tumor
• Pituitary Tumor

git clone https://github.com/TnsaAi/HealthGen.git
cd HealthGen
pip install -r requirements.txt

Organize your dataset:

archive/Training/
├── glioma/
├── meningioma/
├── notumor/
└── pituitary/

python train_medical_classifier.py

python test_system.py

python prepare_dataset.py

python predict.py path/to/image.jpg

• Classifier: ResNet18 (modified for grayscale input)
• Segmentation: UNet XL with encoder-decoder architecture
• Input Size: 128x128 grayscale images
• Output: Disease classification + attention mask

• train_medical_classifier.py - Main training script
• rl_mri_model.py - UNet model definition
• prepare_dataset.py - Dataset preparation utilities
• predict.py - Single image prediction
• requirements.txt - Dependencies

torch>=1.9.0
torchvision>=0.10.0
PIL>=8.0.0
matplotlib>=3.3.0
numpy>=1.21.0
tqdm>=4.62.0

• Training: Mixed precision with data augmentation
• Validation: Real-time accuracy monitoring
• Inference: GPU/CPU compatible

MIT License

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create Pull Request