Innovative Melanoma Diagnosis: Harnessing VI Transformer Architecture

Abstract

Melanoma, the most severe type of skin cancer, ranks ninth among the most prevalent cancer types. Prolonged exposure to ultraviolet radiation triggers mutations in melanocytes, the pigment -producing cells responsible for melanin production. This excessive melanin secretion leads to the formation of dark-colored moles, which can evolve into cancerous tumors over time and metastasize rapidly. This research introduces a Vision Transformer, revolutionizes computer vision architecture by diverging from traditional convolutional neural networks, employing transformer models to handle images as sequences of flattened, spatially-structured patches. The dermoscopy images sourced from the Kaggle repository, an extensive online database known for its diverse collection of high-quality medical imagery is utilized in this study. This novel deep learning model for melanoma classification, aiming to enhance diagnostic accuracy and reduce reliance on expert interpretation. The model achieves an accuracy of 96.23%, indicating strong overall correctness in classifying both Benign and Malignant cases. Comparative simulation of the proposed method against other methods in skin cancer diagnosis reveal that the suggested approach attains superior accuracy. These findings underscore the efficacy of the system in advancing the field of skin cancer diagnosis, offering promising prospects for enhanced accuracy and efficacy in clinical settings.