MetaBayes-SkinNet: A Lightweight Multimodal Framework With Meta-Aware Bayesian-Optimized Augmentation for Multiclass Skin Lesion Classification
International Journal of Imaging Systems and Technology, cilt.36, sa.6, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 36 Sayı: 6
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/ima.70459
- Dergi Adı: International Journal of Imaging Systems and Technology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Bayesian optimization, computer-aided diagnosis, explainable artificial intelligence, meta-aware augmentation, MobileNetV3, multimodal learning, skin lesion classification
- Hakkari Üniversitesi Adresli: Evet
Özet
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.