Malignant Melenoma and Atypical Nevus Classification Using Machine Learning with Shape and Assymetric Features

Abstract

Skin cancer is the most predominat cancer in the world. Detection of skin cancer in earrly stages helps to decrease the fatality rate. Image processing and AI plays vital role in early detection of skin cancer. Dermoscopy images are used by doctors to detect skin cancer.In this paper, shape based features such as eccentricity, perimeter, convex area, orientation,area, ratio of major axis to minor acis length, eqvidiameter have been extracted from segmented skin lesion lesions. Subsequntly these features are give different classifiers such as SVM,KNN,ensemble learning. The efficacy of the proposed method was evaluated on 200 segmented skin lesions of common nevus, atypical nevus and melanoma with different class combinations. The proposed method achieved accuracy of 90% accuracy (acc) and 93% sensitivity (sen) for classifying segmented skin lesions into commatypical nevi and malignant melanoma. This approach helps dermatologists in screening skin lesions very fast and accurate.