A Support Vector Machines based Classifications of the Diabetes Mellitus from Human Tongue Quantitative Features

This paper proposes a new non-invasive automatic method to detect and classify Diabetes Mellitus (DM) in the patients. The proposed automatic method incorporates tongue image processing and LS-SVM based classifier. Color, Texture and Geometry features of healthy and DM tongue images are extracted and analysed. The optimized values of the above feature are used to train the LS-SVM in order to detect and classify the DM automatically. The proposed methods are tested with a new database and the performance of the proposed method was tested with confusion matrix as it gives 85.38% accuracy.