Enhancing Melanoma Detection With Anisotropic Median Filtering and Multinomial Classification Vision Transformer

Abstract

Skin cancer is one of the most prevalent and dangerous types of cancer globally, caused by unrepaired DNA damage leading to abnormal cell growth in the epidermis. Melanoma, in particular, is one of the most hazardous forms, requiring early and precise diagnosis to improve patient outcomes. Early detection and diagnosis are vital for reducing the mortality rates associated with this aggressive cancer. In this paper, we propose a novel approach that combines an anisotropic median filter (AMF) with a modified vision transformer, termed the Multinomial Classification Vision Transformer (MCVT) for skin cancer classification. The AMF is used as pre-processing to effectively remove noise and enhance image quality, preserving critical features essential for accurate classification. On the other hand, the MCVT leverages its robust feature extraction capabilities to classify melanoma with high accuracy. We utilized the HAM10000 dataset for training and evaluation. Our proposed method outperforms existing state-of-the-art techniques, achieving an overall classification accuracy of 91% and a melanoma classification accuracy of 89%. These results demonstrate the potential of integrating AMF and MCVT to enhance skin cancer classification, with a particular focus on improving melanoma detection.