Caries Level Classification using K-Nearest Neighbor, Support Vector Machine, and Decision Tree using Zernike Moment Invariant Features

Abstract

Dental caries is the most common disease and is reported as one of the oldest diseases. To avoid the occurrence of dental caries, there are four ways; maintaining oral hygiene, consuming healthy food, adequate fluoride and giving fracture sealers. Regular dental check-ups can also reduce the risk of developing this disease. In detecting this disease, dentists often fail. This failure was due to the inability to detect early enamel lesions that had not yet developed into cavitation. In this regard, new techniques were developed to help detect this disease. This method uses 10-folds cross validation. This cross validation divides 90% (1256 images) for the train data and 10% (132 images) for the test. In this research using the Zernike moment method for feature extraction. The average results of training accuracy are 94.55%, 84.24%, and 88.46% and the average results of training times are 0.74, 1.63, and 0.77 seconds for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), respectively. This research has obtained perfect performances of classification which are represented with AUC values more than 0.95 for each model.