Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine

This paper proposes an automated system for discrimination between melanocytic nevi and malignant melanoma. The proposed system used a number of features extracted from histo-pathological images of skin lesions through image processing techniques which consisted of a spatially adaptive color median lter for ltering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classied by using support vector machine (SVM) to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The proposed system was able to achieve a good result with classication accuracy of 88.9%, sensitivity of 87.5% and specicity of 100%