MetaBayes ‐ SkinNet : A Lightweight Multimodal Framework With Meta‐Aware Bayesian‐Optimized Augmentation for Multiclass Skin Lesion Classification

Accurate multiclass skin lesion classification is essential for computer‐aided diagnosis (CAD) systems that support early detection and clinical decision‐making. Despite advances in artificial intelligence‐based CAD systems, key challenges persist, including severe class imbalance, limited classification accuracy, high computational cost, and insufficient model interpretability. To address these challenges, this study proposes a lightweight, interpretable, multimodal framework for imbalanced multiclass skin lesion classification. The framework introduces a meta‐aware Bayesian‐optimized RandAugment strategy that applies anatomy‐specific augmentation policies to address class imbalance while preserving clinically plausible variations. A lightweight multimodal MobileNetV3Small architecture integrates dermoscopic image features with clinical metadata through late fusion for efficient representation learning. The resulting multimodal features are classified using a Bayesian‐optimized Light Gradient Boosting Machine (LGBM), and model interpretability is provided through complementary Grad‐CAM and Integrated Gradients visualizations. Experimental evaluation on the HAM10000 dataset demonstrates that the proposed framework achieves 98.17% classification accuracy under lesion‐grouped stratified five‐fold cross‐validation with only 1.35 M parameters and 0.114 GFLOPs, providing highly competitive predictive performance together with substantial computational efficiency. These results indicate that the proposed framework offers a practical, accurate, and interpretable solution for multiclass skin lesion classification, with potential for clinical decision support, subject to prospective external validation.