SAA: A novel skin lesion Shape Asymmetry Classification Analysis

Abstract

INTRODUCTION: Skin cancer is emerging as a significant health risk. Melanoma, a perilous kind of skin cancer, prominently manifests asymmetry in its morphological characteristics. OBJECTIVE: The objective of the study is to classify the asymmetry of the skin lesion shape accurately and to find the number of symmetric lines and the angles of formation of symmetric lines. METHOD: This study introduces a unique methodology known as Shape Asymmetry Analysis (SAA). The SAA incorporates a comprehensive framework including image pre-processing, segmentation along with the computation of mean deviation error and the subsequent categorization of data into symmetric and asymmetric forms using a classification model. RESULT: The PH2 dataset is used in this study, where the three labels are consolidated into two categories. Specifically, the labels "symmetric" and "symmetric with one axis" are merged and classified as "symmetric," while the label "asymmetric" is unchanged and classified as "asymmetric". The model demonstrates superior performance compared to conventional methodologies, achieving a noteworthy accuracy rate of 90%. Additionally, it exhibits a weighted F1-score, precision, and recall of 0.89,0.91,0.90 respectively. CONCLUSION: The SAA model accurately classifies skin lesion shapes compared to state-of-the-art methods. The model can be applied to the shapes, irrespective of irregularity, to find symmetric lines and angles.