Transformers in Skin Lesion Classification and Diagnosis: A Systematic Review
Adebiyi, A. A.; Nader, A.; Simoes, E. J.; Becevic, M.; Hoffman Smith, E.; Rao, P.
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ObjectivesThis systematic review aims to provide a comprehensive overview of the current state of research on the application of transformers in skin lesion classification. Materials and MethodsOver the period 2017-2023, this systematic review investigated the application of transformer-based models in skin lesion classification, focusing on 57 articles retrieved from prominent databases which are PubMed, Scopus, and Medline. The inclusion criteria encompass studies centering on transformer-based models for skin lesion classification, utilization of diverse datasets (dermoscopic images, clinical images, or histopathological images), publication in peer-reviewed journals or conferences, and availability in English. Conversely, exclusion criteria filter out studies not directly related to skin lesion classification, research applying algorithms other than transformer-based models, non-academic articles lacking empirical data, papers without full-text access, and those not in English. ResultsOur findings underscore the adaptability of transformers to diverse skin lesion datasets, the utilization of pre-trained models, and the integration of various mechanisms to enhance feature extraction. ConclusionOur Systematic review showed the implementation and the application of the Transformer models for skin lesion classification. Our study showed the research areas and future research ideas for the use of transformers on skin lesion classification.
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