Detecting Diabetes Mellitus Gradient Vector Flow Snake Segmented Technique

1 Head and Associate Professor, Dept. of Computer Science, Vellalar College for Women, Erode, Tamilnadu, India 2 Research Scholar, Department of Computer Science, Vellalar College for Women, Erode, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract -Diabetes is a chronic disease and a major public health challenge worldwide. Due to lack of awareness among the people on eating habits, diabetic patient counts have been increased steadily in our country. This motivates researchers to develop a medical system which can screen a large number of people for life-threatening disease such as cardiovascular disease, the retinal disorder in diabetic patients. Tongue has played a prominent role in the diagnosis and the subsequent treatment of diseases. Tongue segmentation using Bi-Elliptical Deformable Contour (BEDC) does not process fake edges and also provides poor results. Hence this paper proposes Gradient Vector Flow (GVF) snake technique to extract the region as it encourages convergence in boundary concavities and also provides better results in detecting diabetes mellitus compared to BEDC. Moreover the hybrid classifier using Minimum Distance, Bayes Classifier and Support Vector Machine have been proposed and developed in this research work and gives promising results. The results are evaluated using performance evaluation metrics, Sensitivity and Specificity and gains an accuracy of 85.5% compared to BEDC which has an accuracy of 60%.