Abstract :

Dental Diseases Such As Dental Caries, Periodontal Disease, Periapical Lesions, And Impacted Teeth Are Among The Most Prevalent Oral Health Problems Worldwide. Early And Accurate Diagnosis Is Essential To Prevent Severe Complications; However, Manual Examination Of Dental Radiographs Is Time-consuming And Subject To Interobserver Variability. In Existing Systems, Convolutional Neural Networks (CNNs) Are Widely Used For Imagebased Dental Disease Classification, Achieving Reasonable Accuracy But Lacking Precise Localization Of Diseased Regions.To Address These Limitations, This Research Proposes A YOLOv8-based Deep Learning Framework For Dental Disease Detection And Localization Using Dental Radiographic Images. While CNN Models Focus On Image-level Classification, YOLOv8 Performs Real-time Object Detection, Enabling Both Disease Identification And Spatial Localization. Experimental Analysis Demonstrates That The Proposed YOLOv8 Model Outperforms Traditional CNN-based Approaches In Terms Of Detection Accuracy, Inference Speed, And Robustness, Making It Suitable For Automated Dental Diagnostic Assistance Systems.

Published:

13-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

350-357

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite