Lightweight vision image transformer (LViT) model for skin cancer disease classification

Skin cancer (SC) is a lethal disease not only in India but also in the world; there are more than a million cases of melanoma per year in India. Early detection of skin cancer through accurate classification of skin lesions is essential for effective treatment. Visual inspection by clinical screening, dermoscopy, or histological tests is strongly emphasised in today’s skin cancer diagnosis. It can be challenging to determine the kind of skin cancer, especially in the early stages, due to the resemblance among cancer types. However, the precise classification of skin lesions could be time-consuming and challenging for dermatologists. To address these issues, we propose transfer learning to accurately classify skin lesions into several forms of skin cancer using a lightweight B-16 Vision Image Transformer model (LViT). An extensive dataset is used in the experiment to verify the efficiency of the proposed LViT model. The LViT model can classify skin cancer with high accuracy, sensitivity, and specificity and generalise favourably to new images. The proposed model has a 93.17% accuracy rating for classifying SC images over 25 epochs and a remarkable accuracy of 95.82% over 100 epochs. The proposed LViT model is lightweight, requires minimal processing resources, and achieves good accuracy on small and enormous data sets.