Multimodal Skin Disease Detection

This project integrates dermoscopic images with structured clinical metadata to improve skin disease classification. It combines pretrained CNN backbones, autoencoder-based feature extraction, and fusion models that bring visual and tabular signals together.

Interpretability is a core part of the workflow, so the project includes Grad-CAM and SHAP to show which image regions and metadata features influence each prediction.