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

Abstract

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.