Automated cone cut error detection of bitewing images using convolutional neural network

Mohamed Misbahou Mkouboi, Mohamed Moubarak and Olowolayemo, Akeem and Ghazali, Ahmad Badruddin (2023) Automated cone cut error detection of bitewing images using convolutional neural network. In: National Oral Health Research Initiative Conference (NOHRI) 2023, 20th - 21st October 2023, Kuantan, Pahang, Malaysia. (Unpublished)

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Abstract

Introduction: Cone cut error is one of the technical errors that can hinder the important information from a bitewing radiograph. Meanwhile, deep learning is a specialized artificial intelligence method where an algorithm can be trained to automatically detect, classify and give output based on the trained dataset. Aims: This research aimed to apply deep learning methods to classify cone cut errors from bitewing radiographs. Methods: This study received ethical approval from the IIUM Research Ethics committee: Approval no: IREC 2022-151. 2712 bitewing images were collected and classified into normal and error groups from the Imaging Unit, Kulliyyah of Dentistry, IIUM. The deep learning method selected was Convolutional Neural Network (CNN), and the algorithm was used and trained to classify the cone cut error. Data augmentation was used to increase the amount of data for training, validation, and testing. Results: The test dataset showed good results of 0.93-1.00 recall and precision, while scored 0.96 for the F1 score. Several modifications were made to tailor overfit and unbalanced data groups to get optimum results. Conclusion: This research experimented with an automated approach to utilize deep learning as a method of quality assessment in the dental radiology field, especially in bitewing images. The rapid advance of artificial intelligence should be utilized to improve the imaging quality of a dental radiograph.

Item Type:	Proceeding Paper (Poster)
Uncontrolled Keywords:	artificial intelligence, deep learning, convolutional neural network, dentistry, radiology
Subjects:	R Medicine > RK Dentistry R Medicine > RK Dentistry > RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Dentistry Kulliyyah of Dentistry > Department of Oral Maxillofacial Surgery and Oral Diagnosis Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Ahmad Badruddin Ghazali
Date Deposited:	07 Nov 2023 15:08
Last Modified:	07 Nov 2023 15:08
URI:	http://irep.iium.edu.my/id/eprint/107961

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