A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer

Background/Objectives: Cutaneous melanoma caused over 331,000 new cases and 58,000 deaths worldwide in 2022, with five-year survival falling from 99.4% in localised disease to 35.6% after distant metastasis. Most dermoscopic deep learning studies report headline accuracy without addressing data leakage, calibration, or how their comparisons are constructed. This study asks how much of that performance survives a protocol designed to remove the optimism. Materials and Methods: Six classification architectures were trained on HAM10000/ISIC 2018 (10,015 images, seven classes) at a fixed resolution and with fixed ImageNet-1k pretraining, the architecture confounding with neither. Five segmentation networks were trained alongside MedSAM and evaluated separately under reference-derived and fully automatic box prompts. Lesion-level StratifiedGroupKFold prevented duplicate-lesion leakage; temperature scaling used an inner-validation partition, never the test fold. Models were compared across outer folds (N = 5), saliency localisation against a fixed centred-disc baseline, and generalisation on BCN20000 (15,172 images).