Classification of skin cancer gives the best chance of being diagnosed early. Biopsy method for skin cancer detection is much painful. Human interpretation contains difficulty and subjectivity therefore automated analysis of skin cancer affected images has become important. This paper proposes an automatic medical image classification method to classify two major type skin cancers: Melanoma and Non-melanoma. In this paper, we have used the color and texture features in combination which give better results than using color or gray level information alone. We have used k-means clustering algorithm to segment the lesion. The features are extracted by four different color-texture feature extractors from the segmented images. DullRazor technique is used to eliminate surrounding hairs. Classification accuracy of our proposed system is evaluated on four different types of classifiers and their values are compared with one another. The results of the proposed system are computed on five different classification rates in order to perform better analysis of our proposed system. SVM outperforms among all other classifiers with accuracy of 76.69%.

Grey Level Co-occurrence Matrices, Support Vector Ma-chine, Local Binary Patterns, Texture features, color percentiles, K-means clustering, Cooccurence Matrix, Color Features, Integrative Cooccurence Matrix, Gabor Features, Linear Classifier, NN Classifiers, NMC Classifiers, Cross-validation.

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