Comparison of Dental Caries Level Images Classification Performance using KNN and SVM Methods

Abstract

This study aims to build a dental caries level classification system based on image processing (i.e. to extract texture features) and machine learning methods. The first step was to analyze and discover the extraction results from Gray Level Co-Occurrence Matrix algorithm. After successfully extracting the features, the classification was carried out using a Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Both machine learnings are analyzed and used to obtain the better alternatives of the classification results. This study employed radiographic images of four dental caries classes consisting of Class 1, 2, 3, and 4. Total of images used after pre-processing are 396 images. Training data is 90% of total images then the rest is the testing data. The classification obtained accuracy value of the SVM and KNN. The SVM classification method revealed the highest accuracy value generated by the Fine Gaussian SVM model was 95.7%. Conversely, the lowest accuracy value generated was 83.3%, derived from the Quadratic SVM model. Meanwhile, the highest accuracy by using KNN is 94.9% of accuracy using Fine and lowest accuracy value generated was 91.4%, derived from Weighted KNN models. The KNN classification results are better than the SVM results.