Vision Transformer and Attention-Based Melanoma Disease Classification

This study delves into the critical domain of melanoma detection, a life-saving endeavor that hinges on early diagnosis. Melanoma, a deadly form of skin cancer, poses a formidable challenge due to its tendency to remain dormant until advanced stages. Dermoscopic images serve as valuable tools, but distinguishing melanoma from non-melanoma lesions is notoriously complex. This paper explores the potential of Vision Transformers (ViTs), a novel deep learning architecture equipped with self-attention mechanisms, to enhance melanoma classification. We investigate how ViT's attention mechanisms can capture intricate features in dermoscopic images. The study also delves into fine-tuning strategies specific to medical image analysis. Through rigorous experimentation, our ViT-based sys-tem demonstrates promising results and training accuracy of 97% and testing accuracy of 91% highlighting its potential to revolutionize melanoma diagnosis.