Optimizing Deep Learning for Skin Cancer: A Comparative Study of Convolutional and Attention-Based Models

Skin cancer is among the most prevalent malignancies worldwide, with over 1.5 million new cases estimated in 2022 alone according to GLOBOCAN data ^[1] . Despite the availability of dermoscopy, experienced dermatologists achieve a melanoma detection sensitivity of approximately 75–84% using visual examination, a rate that underscores the diagnostic limitations of unaided clinical assessment ^[2] . This study presents a systematic comparison of five deep learning architectures for the automated classification of seven skin lesion types using the HAM10000 dataset ^[6] , comprising 10,015 dermoscopic images. We evaluate four architectures spanning both convolutional and attention-based paradigms: ResNet-50, EfficientNet-B4, ConvNeXt-Base, Swin Transformer-Base, and Vision Transformer (ViT-B/16). To address the pronounced class imbalance inherent in the dataset, we employed patient-level data partitioning via GroupShuffleSplit to prevent lesion leakage across splits, and WeightedRandomSampler during training. All models were trained using AdamW optimization with label smoothing and mixed-precision training. Transformer-based architectures were further stabilized through linear warmup scheduling and stochastic depth regularization. Our best single model, ViT-B/16, achieved a test accuracy of 85.66% and a macro AUC-ROC of 0.9629. An ensemble of EfficientNet-B4 and Swin Transformer-Base achieved the highest overall performance with a test accuracy of 86.57%, a balanced accuracy of 79.98%, a macro F1-score of 0.7856, and a macro AUC-ROC of 0.9811. These results demonstrate that heterogeneous ensemble strategies combining architecturally diverse models offer a meaningful improvement over individual classifiers in dermoscopic lesion classification.