Objectives: This 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 Methods: Over 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. Results: Our 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. Conclusion: Our 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.

The authors have declared no competing interest.

This project was funded by the Translational Research Informing Useful and Meaningful Precision Health (TRIUMPH) grant at the University of Missouri-Columbia.

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