Classification of Oral Submucous Fibrosis using SVM

Medical images form an essential source of information for various important tasks such as diagnosis of diseases, surgical planning, medical reference, research and training. Oral submucous fibrosis [OSMF] is a chronic debilitating disease of the oral cavity characterized by inflammation and progressive fibrosis of the submucosal tissues. Support Vector Machine [SVM] is a statistic machine learning technique that has been successfully applied in the pattern recognition and is based on the principle of structural risk minimization. In this paper a histogram based feature extraction technique has been proposed to classify normal images and OSMF affected images using SVM. An attempt is made to provide an enhanced knowledge about computer aided diagnosis of this potentially malignant disorder, to health care providers in order to help in differentiating the OSMF affected tissue from normal. Experiments showed significantly satisfactory results with an accuracy of 94%.