SVMs classification based on Insitu melanoma

This paper presents a comparative study of Support Vector Machines (SVMs) which is classified based on melanoma imaging technique. After the preprocessing and segmentation of a set of distinct 35 images, the extracted features were Asymmetry, Border, Color, Diameter,(ABCD) Entropy and Correlations respectively. Further the resultant data was fed into five different SVM classifiers namely linear, poly, quadratic, radial basic function and Multilayer Preceptor, which classifies different aspects of Insitu melanoma. Among these 35 images the most consistent result which was obtained stood unmatched at an accuracy of 77.77%. Finally each stage of pigmented skin lesion is represented by a histogram plot which further enriches the data quite significantly.