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Deep learning architectures in dental diagnostics: a systematic comparison of techniques for accurate prediction of dental disease through x-ray imaging

Muhammad Adnan Hasnain (National College of Business Administration and Economics, Lahore, Pakistan)
Hassaan Malik (National College of Business Administration and Economics, Lahore, Pakistan)
Muhammad Mujtaba Asad (Sukkur IBA University, Sukkur, Pakistan)
Fahad Sherwani (National University of Computer and Emerging Sciences, Karachi, Pakistan)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 30 October 2023

Issue publication date: 29 February 2024

183

Abstract

Purpose

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

Design/methodology/approach

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Findings

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

Practical implications

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

Originality/value

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

Keywords

Citation

Hasnain, M.A., Malik, H., Asad, M.M. and Sherwani, F. (2024), "Deep learning architectures in dental diagnostics: a systematic comparison of techniques for accurate prediction of dental disease through x-ray imaging", International Journal of Intelligent Computing and Cybernetics, Vol. 17 No. 1, pp. 161-180. https://doi.org/10.1108/IJICC-08-2023-0230

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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