Identifying the role of vision transformer for skin cancer—A scoping review

Introduction Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- inter-observer variabilities. Dermoscopy images are widely used identify study skin cancer, but the blurred boundaries between besieging tissues can lead incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, variations in symptoms underlying effects hinder their performance. Objective This scoping review synthesizes analyzes literature that uses transformers for lesion detection. Methods The follows PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses Extension Scoping Revise) guidelines. searched online repositories such IEEE Xplore, Scopus, Google Scholar, PubMed retrieve relevant articles. After screening pre-processing, 28 studies fulfilled inclusion criteria were included. Results discussions found use of cancer detection has rapidly increased from 2020 2022 shown outstanding performance using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular shapes, many other unwanted challenges, also discusses key problems obfuscate trustworthiness diagnosis. provides new insights practitioners researchers understand current state knowledge this specialized research domain outlines best segmentation techniques accurate perform melanoma These findings will ultimately assist making more authentic decisions promptly.