Explainable Multimodal Deep Learning for Skin Lesion Diagnosis Using Hybrid Adam–GWO

Early and accurate skin cancer diagnosis is critical for improving patient outcomes, yet current deep learning classifiers often suffer from limited convergence stability, underutilize patient-level metadata, and lack traceable diagnostic reasoning. To overcome these challenges, this study presents an explainable multimodal deep learning framework optimized via a hybrid Adam–Gray Wolf Optimization (Adam–GWO) strategy. The framework integrates dermoscopic image representations with structured patient metadata (age, sex, anatomical site) through feature-level fusion. The hybrid optimizer combines Adam mini-batch gradient updates with epoch-wise GWO validation-guided swarm exploration for dynamic feature mask gating and hyperparameter tuning. Explainability is established using Grad-CAM++ visual localization maps alongside SHAP metadata feature attributions. To prevent data leakage, evaluation was conducted using group-stratified splits (70% training, 15% validation, 15% testing) based on patient and lesion identifiers across five fixed random seeds on the HAM10000 and SIIM–ISIC 2020 benchmark datasets. Across VGG16, ResNet-50, InceptionV3, and Vision Transformer (ViT-B/16) backbones, the proposed Adam–GWO strategy consistently outperformed baseline optimizers and alternative hybrid methods. On SIIM–ISIC 2020, InceptionV3 with Adam–GWO achieved 89.4 ± 0.6% classification accuracy and 0.945 ± 0.007 AUC, outperforming image-only (87.1%, 0.920 AUC) and metadata-only (72.0%, 0.776 AUC) baselines. On HAM10000, ViT-B/16 achieved 94.0 ± 0.5% accuracy and 0.973 ± 0.004 AUC. Computational profiling indicated a modest training overhead of 9.7–10.7% per epoch, while embedded feature selection reduced single-image real-time inference latency to 11.8 ms/image. Overall, the proposed framework demonstrates a robust balance of predictive performance, empirical reproducibility, computational efficiency, and clinical interpretability.