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

Publisher:
CIMSim2013
Publication Type:
Conference Proceeding
Citation:
2013, pp. 236 - 241 (6)
Issue Date:
2013
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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 classied 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 classication accuracy of 88.9%, sensitivity of 87.5% and specicity of 100%.
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