Melanoma Classification Using Multiwavelet Transform and Support Vector Machine


  • Shamganth Kumarapandian


Melanoma Skin Cancer Classification, Multi Wavelet Transform, Statistical Features, Support Vector Machine


The skin exposed to the sun is the main cause. It spreads very fast, so early diagnosis of skin cancer is required for affected persons. In this study, an efficient method for Melanoma Skin Cancer Classification (MSCC) system is presented. MSCC system uses wiener filter for preprocessing, Multi Wavelet Transform (MWT), statistical features (mean, standard deviation and variance) for feature extraction and Support Vector Machine (SVM) classifier is used for classification. Initially, the input melanoma images are given to wiener filter to remove the hair in the skin. The preprocessed melanoma images are decomposed by MWT. The subband coefficients of MWT are extracted by mean, standard deviation and variance. Finally, SVM is used for the classification of melanoma images into normal and abnormal. Performance of MSCC system is measured by classification accuracy, sensitivity and specificity by using MWT and SVM.


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Author Biography

Shamganth Kumarapandian

Head of Section, Electrical and Electronic Section, IBRA College of Technology, Oman


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