A novel classification system for dysplastic nevus and malignant melanoma

Abstract

Melanoma is a potentially deadly form of skin cancer, however, if detected early, it is curable. A dysplastic nevus (atypical mole) is not cancerous but may represent a precursor to malignancy as nearly 40% of melanomas arise from a preexisting mole. In this study, we propose a system to classify a skin lesion image as melanoma (M), dysplastic nevus (D), and benign (B). For this purpose we develop a new two layered-system. The first layer consists of three binary Support Vector Machine (SVM) classifiers, one for each pair of classes, M vs B, M vs D, and B vs D. The second layer is a novel decision maker function, which uses probability memberships derived from the first layer. Each lesion is characterized with five features, which mostly overlaps with the ABCD rule of dermatology. The dataset we used have 112 lesions with 54 M, 38 D, and 20 B cases. In the experiments of melanoma detection, we obtained 98% specificity, 76% sensitivity, and 85% F-measure accuracy.