GLCM and Multi Class Support vector machine based automated skin cancer classification

Abstract

It is utmost important to early detect the skin cancers. Proper diagnosis is critical for survival of the patient. Biopsy method of detection is much painful. We have proposed an automated system for detection and classification of one of the skin four types of skin cancers: Melanoma, Basal cell carcinoma, actinic Keratosis, Squamous cell carcinoma. There are a certain features of these types of skin cancers, which can be extracted using proper feature extraction algorithm. The features of skin lesions are extracted normalized symmetrical Grey Level Co-occurrence Matrices (GLCM). GLCM based texture features are extracted from each of the four classes and given as input to the Multi-Class Support vector machine which is used for c1assification purpose. It c1assifies the given data set into one of the four skin cancer classes. The accuracy of our proposed method is 81.43%.