Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network
Malocclusion 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...
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| Format: | Article |
| Language: | English |
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HH Publisher
2022-10-01
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| Series: | Progress in Microbes and Molecular Biology |
| Online Access: | https://journals.hh-publisher.com/index.php/pmmb/article/view/649 |
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| author | Muhamad Farhin Harun Azurah A Samah Muhammmad Imran Ahmad Shabuli Hairudin Abdul Majid Haslina Hashim Nor Azman Ismail Syiral Mastura Abdullah Aspalilah Alias |
| author_facet | Muhamad Farhin Harun Azurah A Samah Muhammmad Imran Ahmad Shabuli Hairudin Abdul Majid Haslina Hashim Nor Azman Ismail Syiral Mastura Abdullah Aspalilah Alias |
| author_sort | Muhamad Farhin Harun |
| collection | DOAJ |
| description |
Malocclusion 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.
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| format | Article |
| id | doaj-art-cec7bfac90ef4995878d502d708149fd |
| institution | Kabale University |
| issn | 2637-1049 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | HH Publisher |
| record_format | Article |
| series | Progress in Microbes and Molecular Biology |
| spelling | doaj-art-cec7bfac90ef4995878d502d708149fd2025-02-04T08:40:05ZengHH PublisherProgress in Microbes and Molecular Biology2637-10492022-10-015110.36877/pmmb.a0000279Incisor Malocclusion Using Cut-out Method and Convolutional Neural NetworkMuhamad Farhin HarunAzurah A SamahMuhammmad Imran Ahmad ShabuliHairudin Abdul MajidHaslina HashimNor Azman IsmailSyiral Mastura AbdullahAspalilah Alias Malocclusion 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. https://journals.hh-publisher.com/index.php/pmmb/article/view/649 |
| spellingShingle | Muhamad Farhin Harun Azurah A Samah Muhammmad Imran Ahmad Shabuli Hairudin Abdul Majid Haslina Hashim Nor Azman Ismail Syiral Mastura Abdullah Aspalilah Alias Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network Progress in Microbes and Molecular Biology |
| title | Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network |
| title_full | Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network |
| title_fullStr | Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network |
| title_full_unstemmed | Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network |
| title_short | Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network |
| title_sort | incisor malocclusion using cut out method and convolutional neural network |
| url | https://journals.hh-publisher.com/index.php/pmmb/article/view/649 |
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