HYBRID DEEP LEARNING AND OPTIMIZATION-DRIVEN FRAMEWORK FOR ENHANCED SKIN CANCER DETECTION AND CLASSIFICATION

Globally, skin cancer is one of the most common diseases caused by excessive exposure to ultraviolet (UV) radiation. The outcomes and prognosis for treating skin malignancies significantly improve with early detection and accurate diagnosis. However, many diagnostic methods have limitations, entail high computational costs, rely heavily on manual feature extraction, lack good generalization across datasets, and are susceptible to adversarial attacks. This paper addresses these limitations in skin cancer diagnosis by proposing an improved Wavelet-AHE Diffusion Enhanced Hybrid Network (WADE-HNet) for enhancing detection as well as classification. The proposed ensemble technique integrates ResEff-FuseNet for feature extraction, Firefly-Bitterling Adaptive Selection Optimization (FBASO) for optimal feature selection, Multi-Stage Attention Capsule Network (MSA-CapsNet) for enhanced classification performance, and Modified U-Net++ for lesion segmentation. Such improvements increase the interpretability, generalizability, and computational efficiency of model performance. Finally, empirical results for both the HAM10000 and ISIC 2019 datasets validate that WADE-HNet outperforms benchmark models with a high accuracy of 99.39%. In this way, the proposed strategy ensures consistency in clinical usage while reducing false positives and false negatives.