Huntington's Disease (HD) is a hereditary neurodegenerative disorder caused by abnormal CAG repeat expansions in the HTT gene. Early diagnosis is essential for effective disease management and clinical intervention.
This project presents an Explainable Multimodal Deep Learning Framework that combines brain MRI images and genetic data to improve Huntington's Disease prediction. The model integrates EfficientNet-B3 for MRI feature extraction, Graph Neural Networks (GNN) for genetic relationship modeling, and a Transformer-based Cross-Attention Fusion mechanism for multimodal learning.
- Multimodal analysis using MRI and genetic data
- EfficientNet-B3 for brain image feature extraction
- Graph Neural Network (GNN) for genetic data analysis
- Cross-Attention Fusion for combining multimodal features
- Explainable AI using SHAP and Grad-CAM
- Early and accurate Huntington's Disease prediction
- Brain MRI images
- HTT gene DNA sequences
- MRI resizing, normalization, and denoising
- Genetic sequence encoding
- EfficientNet-B3 for MRI features
- GNN for genetic feature learning
- Transformer-based Cross-Attention Fusion
- Deep Neural Network for disease prediction
- SHAP for genomic feature interpretation
- Grad-CAM for MRI visualization
- Python
- TensorFlow / Keras
- EfficientNet-B3
- Graph Neural Networks (GNN)
- Transformer Networks
- SHAP
- Grad-CAM
- NumPy
- Pandas
- OpenCV
- Scikit-learn
- Integration of additional biomarkers such as EEG and clinical data
- Real-time clinical decision support systems
- Edge and mobile deployment for remote healthcare monitoring
- Enhanced transformer-based multimodal architectures
- Early Huntington's Disease prediction
- Clinical decision support systems
- AI-assisted healthcare diagnostics
- Precision medicine research