Explainable Hybrid Feature Fusion Network for Automated Skin Cancer Detection from Dermoscopic Images

A fair and explainable hybrid framework integrating EfficientNetV2L deep image features, handcrafted computer
vision descriptors, and patient metadata for multi-class
skin lesion classification.
• A preprocessing pipeline incorporating hair artifact removal and data augmentation to improve image quality
and address class imbalance.
• Integration of LIME and SHAP for local and global
interpretation of model predictions.
• Fairness-aware evaluation across estimated skin-tone
groups.