TONGUE IMAGE CLASSIFICATION USING RANDOM FOREST CLASSIFIER
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 9, Pages 1435-1438
AbstractDiabetes 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.
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