A Comparative Analysis of CNN Models and Vision Transformers for Skin Lesion Classification - Aguirre, Yasmin C.; Oliveira, Ashiley Bianca S. de; Barros, Rodrigo C.; Kupssinskü, Lucas S. (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS), 2025)
Skin cancer is one of the most common malignancies in Brazil, making accurate diagnosis essential. This study evaluates five convolutional neural networks (CNNs) and two Vision Transformers (ViTs) on the binary classification of dermoscopic images (nevus vs. melanoma) from four public datasets characterized by class imbalance. Using a unified fine-tuning protocol and hold-out splits, we observed that ViTs generally outperformed CNNs in F1-score and ROC-AUC. These results suggest that attention-based models may provide more balanced performance under class imbalance.