Publication:
Incisor Malocclusion Using Cut-out Method And Convolutional Neural Network

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dc.contributor.authorMuhamad Farhin Harunen_US
dc.contributor.authorAzurah A Samahen_US
dc.contributor.authorMuhammad Imran Ahmad Shabulien_US
dc.contributor.authorHairudin Abdul Majiden_US
dc.contributor.authorHaslina Hashimen_US
dc.contributor.authorNor Azman Ismailen_US
dc.contributor.authorSyiral Mastura Abdullahen_US
dc.contributor.authorAspalilah Aliasen_US
dc.date.accessioned2024-05-29T02:28:36Z
dc.date.available2024-05-29T02:28:36Z
dc.date.issued2022
dc.date.submitted2023-2-8
dc.descriptionVol. 5 No. 1en_US
dc.description.abstractMalocclusion is a condition of misaligned teeth or irregular occlusion of the upper and lower jaws. This condition leads to poor performance of vital functions such as chewing. A common procedure in orthodontic treatment for malocclusion is a conventional diagnostic procedure where a dental health professional takes dental x-rays to examine the teeth to diagnose malocclusion. However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. However, several issues concerning the dental images as input of the classification model may affect the accuracy of the classification. In addition, unstructured images with varying sizes and the problem of a machine learning algorithm that does not focus on the region of interest (ROI) for incisor features bring challenges in delivering the treatment. This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. From the results, the implementation of the cut-out method generates higher accuracy across all classes of malocclusion compared to the non-implementation of the cut-out method.en_US
dc.identifier.doi10.36877/pmmb.a0000279
dc.identifier.epage16
dc.identifier.issn2637-1049
dc.identifier.issue1
dc.identifier.spage1
dc.identifier.urihttps://journals.hh-publisher.com/index.php/pmmb/article/view/650
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10770
dc.identifier.volume5
dc.language.isoen_USen_US
dc.publisherHH Publisheren_US
dc.relation.ispartofProgress in Microbes and Molecular Biologyen_US
dc.subjectMalocclusion; orthodontics; incisor; classification; convolutional neural network; class activation mapping, cut-out method; image processingen_US
dc.titleIncisor Malocclusion Using Cut-out Method And Convolutional Neural Networken_US
dc.typeArticleen_US
dspace.entity.typePublication

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