Performance Comparison of Machine Learning Algorithms Using EfficientNetB0 Feature Extraction on Dental Disease Classification

Abstract

Oral health conditions such as dental caries, calculus, gingivitis, and ulcers are prevalent globally and require accurate early detection to prevent further complications. Traditional diagnostic methods such as visual inspection and manual radiograph analysis often rely on subjective judgment, leading to inconsistencies, delayed treatment, and limited accessibility, particularly in underserved areas. This study proposes an intelligent classification framework for dental disease detection based on intraoral images. Deep features were extracted using EfficientNetB0, followed by classification through eleven machine learning algorithms, including SVM, XGBoost, and K-Nearest Neighbors. Preprocessing steps included image augmentation, SMOTE for class balancing, and feature normalization. Among all models, SVM achieved the highest accuracy of 92,9%, while XGBoost and LightGBM followed closely at 91.3%. Using K-Fold Cross Validation, KNN algorithm has an increasing value with accuracy of 91,24%. This indicate the KNN algorithm able to tackle generalization problem towards the classification. The results demonstrate that features extracted using CNNs, when classified using machine learning algorithms, can provide a scalable and effective alternative to conventional diagnostic practices. Hence, Machine Learning algorithms provide a promising result towards dental disease classification.