Skin cancer classification using vision transformers and explainable artificial intelligence

Background: Skin cancer diagnosis is a critical aspect of dermatological healthcare, and requires accurate and efficient classification methods. Recently, vision transformers (ViTs) and convolutional neural networks (CNNs) have emerged as promising architectures. However, the interpretability of these models remains a concern, hindering their widespread adoption in the clinical setting. Therefore, the aim of this research is to propose an explainable skin cancer classification using deep learning and explainable artificial intelligence methods.