Automated classification radiograph of Periodontal bone loss using deep learning

Al Husaini, Mohammed Abdulla Salim and Habaebi, Mohamed Hadi and Yadav, Seema (2025) Automated classification radiograph of Periodontal bone loss using deep learning. Biomedical Engineering and Computational Biology, 16 (12). pp. 1-12. ISSN 1179-5972

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Abstract

Background: Periodontitis is a common chronic inflammatory condition of the supporting tissues of the teeth that destroys the tissues and, if left untreated, results in tooth loss. Accurate and early classification of periodontal bone loss through dental radiographs, such as orthopantomograms (OPGs), is crucial for effective diagnosis and treatment planning. Objectives: The present study aimed to evaluate and compare 3 deep learning architectures—InceptionV3, InceptionV4, and ResNet-50—for classifying OPGs into distinct grades of dental features characterised by periodontal bone loss. Design: A comparative experimental design was adopted to analyse the performance of multiple convolutional neural network architectures trained on OPG images representing various grades of periodontal conditions. Methods: A deep convolutional neural network architecture with varying filter and feature layers was implemented. The training process was conducted using MATLAB on a Dell computer equipped with a GeForce RTX 4060 GPU. Image data augmentation was applied to increase dataset diversity. Several combinations of epochs, learning rates, and optimisation algorithms were tested to enhance performance. Model evaluation metrics included accuracy, precision, recall, and F1-score. Results: Among the tested architectures, ResNet-50 achieved superior performance, reaching an accuracy of 96.8% by the 16th epoch when trained using an SGD optimiser with momentum and a learning rate of 0.001. It also demonstrated higher precision, recall, and F1 scores compared to InceptionV3 and InceptionV4, confirming its effectiveness in OPG classification. Conclusion: The findings indicate that ResNet-50 provides better classification accuracy and reliability than InceptionV3 and InceptionV4 in detecting periodontal bone loss from OPG images. Expanding the dataset and exploring advanced data augmentation and hyperparameter tuning could further improve model robustness. This study highlights the potential of deep learning-based OPG classification systems to assist dental professionals in faster and more accurate detection of periodontal diseases.

Item Type:	Article (Journal)
Additional Information:	6727/126347
Uncontrolled Keywords:	deep learning, OPGS, periodontal bone loss, optimisation methods, ResNet-50
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	30 Dec 2025 21:12
Last Modified:	30 Dec 2025 21:12
Queue Number:	2025-12-Q1251
URI:	http://irep.iium.edu.my/id/eprint/126347

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