Integration of Fractal Dimension in Vision Transformer for Skin Cancer Classification

Abstract

In order to classify skin lesions, many efforts have been made to create various automated diagnostic systems. For that purpose many efforts have been put in creating various automated diagnostics systems Nowadays, with the rapid advancements in deep learning, Vision Transformers have emerged as powerful models for image processing and analysis purposes. This type of model has already proved useful for cancer detection and classification tasks in particular. However, the complexity and variability of skin lesions present significant challenges in accurately classifying them. Integrating the concept of fractal dimension into Vision Transformers can potentially improve their performance by capturing the intricate structural patterns of skin lesions. This paper aims to explore the integration of fractal dimension metrics into a Vision Transformer for skin cancer classification. The problem at hand is to investigate the integration of fractal dimension metrics into the existing Vision Transformer architecture for the accurate classification of skin lesions as cancerous or non-cancerous. Fractal dimensions provide a measure of the complexity and irregularity of an object, which can be informative in characterizing skin cancer lesions. We aim to research possability and ways of incorporating fractal dimension metrics into the Vision Transformer model for results improvements.