Comparison of Leading Deep Learning Architectures on Skin Lesion Classification Using the ISIC 2019 Dataset

This paper addresses the problem of automatic classification of dermoscopic skin lesion images into one of eight diagnoses that include multiple cancer types. The goal of this research was to evaluate methods to address problems of class imbalances and image variation in skin lesion classification, as well as to propose an ensemble model that performs well on multi-class metrics. The widely used dataset developed for the International Skin Imaging Collaboration (ISIC) challenge, ISIC 2019, was chosen to test the methods proposed in this paper. Methods evaluated in this paper include modern architectures such as Vision Transformer, Convolutional Neural Networks, and Vision Mamba, as well as training strategies such as data augmentation, test-time augmentation, and specialized loss functions addressing class imbalance. The best performance was obtained using an ensemble approach, achieving an overall accuracy of 74.80% and a macro F1 score of 74.30%. These results compare favorably to the state-of-the-art, where drops in performance with multi-class metrics have typically been observed.