Automatic non-invasive recognition of melanoma using Support Vector Machines

Abstract

This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Grey Level Co-occurrence Matrices (GLCM) of the gray-scale skin lesion images, and color features obtained from the original color images. The dataset used include digital images for skin lesions that are either benign or malignant. The purpose of this project is to provide a system that can classify digital images of skin lesions as benign or malignant (Melanoma). The testing accuracy obtained by the Support Vector Machine classifier used in this experiment is 82.7% for the GLCM features and 81.48% for both GLCM and Color features using ROI segmentation. The proposed system has resulted in a sensitivity of 83.6 % for the case of GLCM and 83.33% for the case of GLCM and color using ROI segmentation. It has also resulted in a specificity of 80% for the case of GLCM and 76.19% for the case of both GLCM and Color features using ROI segmentation. The obtained sensitivity and specificity results are comparable to those obtained by dermatologists. Consequently, this can increase the chance of the survival from Melanoma.