EVALUATION OF THE PERFORMANCE OF MACHINE LEARNING IN CLASSIFICATION OF IMAGES WITH OR WITHOUT MISSING TEETH IN PANORAMIC RADIOGRAPHS

Objective: This study aims to evaluate the functionality and usability of machine learning (ML) in classifying missing teeth in panoramic radiography. Material and Method: In this study, of 1000 anonymous panoramic radiographs archived for the classification of missing teeth, 500 contained missing teeth, while the other 500 did not contain missing teeth. 700 of the images are reserved for training and 300 for testing. Principal component analysis (PCA) was used to extract features from panoramic radiographs. Six different classification model algorithms (Support Vector Machines (SVM), Random Forest Classifier, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, and Gaussian NB) were used for missing/complete tooth classification on the created data set. The performance of these models was evaluated. Results: Among the classification models included in the study, the accuracy scores of SVM were found to be higher than other algorithms, with 98.14% in the training data set and 81.67% in the test data set. Conclusion: The selection of the appropriate machine learning model is very important to ensure accurate and reliable diagnosis in the field of medical image classification. SVM is a very successful method in classifying multidimensional data.