Bio-inspired optimization and AI interpretability for melanoma classification

The early and precise diagnosis of melanoma plays a crucial role in improving survival rates and enabling timely intervention. However, automated melanoma detection systems face challenges due to image artifacts, illumination variation, and inter-class similarities. This study introduces a generalized bio-inspired optimization and deep learning framework that integrates multiple metaheuristic algorithms to enhance model learning and interpretability for melanoma classification. The proposed approach optimizes critical parameters in deep convolutional neural networks (CNNs), improving convergence speed, feature selection, and generalization. Optimization algorithms dynamically tune hyperparameters such as learning rate, dropout, and dense layer configuration to achieve an optimal exploration–exploitation balance.

For interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is applied to visualize the discriminative regions influencing the model’s decisions, enhancing clinical transparency and reliability. The experimental analysis utilizes publicly available dermoscopic datasets for melanoma detection, with comprehensive comparisons across models optimized by different heuristic algorithms. The bio-inspired optimized deep models demonstrate significant improvements in accuracy, precision, recall, and AUC–ROC, with the best-performing configuration achieving over 99% classification accuracy.

Furthermore, statistical and visual analyses confirm the robustness, stability, and interpretability of the optimized models. The proposed framework not only enhances melanoma detection performance but also contributes to explainable AI (XAI) development in dermatology, bridging the gap between computational intelligence and clinical trust. This synergy between bio-inspired optimization and AI-driven interpretability demonstrates potential for integration into real-time diagnostic systems and teledermatology applications, advancing intelligent healthcare automation.