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

TL;DR

This paper presents a novel skin cancer classification system using Vision Transformer and segmentation techniques, achieving high accuracy and robustness for non-invasive dermatoscopy analysis.

Contribution

It introduces a Vision Transformer-based model tailored for skin cancer detection, integrating segmentation and classification for improved diagnostic performance.

Findings

Achieved 96.15% accuracy in skin cancer classification.

Utilized the HAM10000 dataset with effective preprocessing.

Demonstrated superior performance over traditional models.

Abstract

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.