  Skin cancer is a global health concern, necessitating early and accurate
diagnosis for improved patient outcomes. This study introduces a groundbreaking
approach to skin cancer classification, employing the Vision Transformer, a
state-of-the-art deep learning architecture renowned for its success in diverse
image analysis tasks. Utilizing the HAM10000 dataset of 10,015 meticulously
annotated skin lesion images, the model undergoes preprocessing for enhanced
robustness. The Vision Transformer, adapted to the skin cancer classification
task, leverages the self-attention mechanism to capture intricate spatial
dependencies, achieving superior performance over traditional deep learning
architectures. Segment Anything Model aids in precise segmentation of cancerous
areas, attaining high IOU and Dice Coefficient. Extensive experiments highlight
the model's supremacy, particularly the Google-based ViT patch-32 variant,
which achieves 96.15% accuracy and showcases potential as an effective tool for
dermatologists in skin cancer diagnosis, contributing to advancements in
dermatological practices.
