European Journal of Molecular & Clinical Medicine

Diabetes people who also take antibiotics to combat different infections are particularly vulnerable to fungal mouth and tongue infection. The fungus prospers in the saliva of uncontrolled diabetes to high glucose levels. An efficient method for Tongue image classification using Non-Negative Matrix Factorization (NNMF) and various Support Vector Machine (SVM) kernels are presented in this study. The input tongue images are given to NNMF for feature extraction and stored in feature database. Finally, SVM kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) are used for prediction. The system produces the classification accuracy of 92% by using NNMF and different SVM kernels

Diabetes people who take antibiotics regularly to treat multiple infections are more likely to develop a fungal mouth and language infection. The fungus thrives in people with uncontrolled diabetes at high levels of glucose in the saliva. A yeast infection called oral thrush is common among people with diabetes. It looks like a white layer coating your language and your cheeks' insides. The Yeast grows in a higher amount of sugar found in your saliva. The early diagnosis is required for tongue image classification. In this study, the automatic classification of tongue image classification for diabetes detection system is discussed. Initially, the input tongue images are given to median filter for pre-processing. Then the Gray Level Co-occurrence Matrix (GLCM) and Haralick features are extracted. Finally, Random Forest (RF) classifier is used for Prediction. The performance of proposed system produces the classification accuracy of 95%using RF classifier.

In the day to day working of people, the tongue plays a significant role.The tongue is an organ connected to each other parts.Diabetics classification using tongue images are described in this study. Diabetes detection using tongue image using extraction of texture and random forestis described in this study.Initially, the input tongue images are given to global feature for feature extraction and finally, the decision treeclassifieris used for classification. Experimental results show the performance of proposed system using texture and RFC.