Transforming Skin Cancer Diagnosis: Leveraging Vision Transformers for Accurate Classification

Identifying malignant and benign lesions accurately and promptly is crucial when diagnosing skin cancer in dermatology. In this research, we explore how effective Vision Transformers (ViTs) are in automated skin cancer categorization, utilizing the transformer structure initially created for tasks in natural language processing. Our study is centered on fine-tuning the pre-trained ViT-B16 model using a dataset that includes pictures of both malignant and benign skin lesions. Through the use of transfer learning methods, the ViT- B16 model is customized for the particular job of categorizing skin cancer, showcasing its capacity to comprehend overall contextual details and differentiate among various lesion varieties. Performance evaluation metrics like accuracy, precision, recall, F1 score, and Matthews correlation coefficient are used to evaluate how well the model can predict outcomes. The results from experiments show that the ViT-B16 model has strong performance, with high accuracy scores, suggesting it could be a valuable automated tool for diagnosing skin cancer. This research adds to the increasing amount of studies investigating deep learning methods in dermatology and emphasizes the revolutionary effects of Vision Transformers in analyzing medical images.