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

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Conference Proceeding
Proceedings of the 5th International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 236 - 241
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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 filter for filtering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classified 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 classification accuracy of 88.9%, sensitivity of 87.5% and specificity of 100%.
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