Lightweight Deep Learning for Skin Lesion Image Classification

Skin lesion image classification is important for early skin-cancer screening, but dermoscopic datasets are severely class-imbalanced, which can impair the recognition of minority classes such as melanoma. Using MobileNetV2 as the backbone, this paper studies, through ablation experiments, how channel attention (squeeze-and-excitation, SE) and class-imbalance handling strategies affect seven-class skin lesion classification. On the HAM10000 dataset split by lesion identifier, class-balanced weighted cross-entropy raises the macro-F1 from 0.702 to 0.776, and class-balanced sampling further raises it to 0.797, minority-class recall improves (melanoma 0.514 to 0.577, vascular lesions 0.467 to 0.867). SE alone improves class balance, but its gain does not add up with the imbalance strategies. The results provide empirical guidance for comparing structural enhancements and imbalance-handling strategies in lightweight skin lesion classification.