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Skin cancer is a global health concern, necessitating early and accuratediagnosis for improved patient outcomes. This study introduces a groundbreakingapproach to skin cancer classification, employing the Vision Transformer, astate-of-the-art deep learning architecture renowned for its success in diverseimage analysis tasks. Utilizing the HAM10000 dataset of 10,015 meticulouslyannotated skin lesion images, the model undergoes preprocessing for enhancedrobustness. The Vision Transformer, adapted to the skin cancer classificationtask, leverages the self-attention mechanism to capture intricate spatialdependencies, achieving superior performance over traditional deep learningarchitectures. Segment Anything Model aids in precise segmentation of cancerousareas, attaining high IOU and Dice Coefficient. Extensive experiments highlightthe model's supremacy, particularly the Google-based ViT patch-32 variant,which achieves 96.15% accuracy and showcases potential as an effective tool fordermatologists in skin cancer diagnosis, contributing to advancements indermatological practices.

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System