Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs
Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an im...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9940956/ |
| _version_ | 1811226034719162368 |
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| author | Shih-Lun Chen Tsung-Yi Chen Yen-Cheng Huang Chiung-An Chen He-Sheng Chou Ya-Yun Huang Wei-Chi Lin Tzu-Chien Li Jia-Jun Yuan Patricia Angela R. Abu Wei-Yuan Chiang |
| author_facet | Shih-Lun Chen Tsung-Yi Chen Yen-Cheng Huang Chiung-An Chen He-Sheng Chou Ya-Yun Huang Wei-Chi Lin Tzu-Chien Li Jia-Jun Yuan Patricia Angela R. Abu Wei-Yuan Chiang |
| author_sort | Shih-Lun Chen |
| collection | DOAJ |
| description | Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0. |
| first_indexed | 2024-04-12T09:17:19Z |
| format | Article |
| id | doaj.art-a81c9db05c644ed59eb52051f5eca498 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T09:17:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a81c9db05c644ed59eb52051f5eca4982022-12-22T03:38:48ZengIEEEIEEE Access2169-35362022-01-011011865411866410.1109/ACCESS.2022.32203359940956Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNsShih-Lun Chen0https://orcid.org/0000-0002-4079-9350Tsung-Yi Chen1Yen-Cheng Huang2Chiung-An Chen3https://orcid.org/0000-0002-7605-5214He-Sheng Chou4Ya-Yun Huang5Wei-Chi Lin6Tzu-Chien Li7Jia-Jun Yuan8Patricia Angela R. Abu9https://orcid.org/0000-0002-8848-6644Wei-Yuan Chiang10https://orcid.org/0000-0001-5158-0031Department of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of General Dentistry, Chang Gung Memorial Hospital, Taoyuan, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Chung-Li, TaiwanDepartment of Information Systems and Computer Science, Ateneo de Manila University, Quezon, PhilippinesDepartment of Electronic Engineering, Ming Chi University of Technology, New Taipei, TaiwanCommon dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0.https://ieeexplore.ieee.org/document/9940956/Biomedical imagepanoramic imagehistogram equalizationflat-field correctiontooth segmentationtooth position |
| spellingShingle | Shih-Lun Chen Tsung-Yi Chen Yen-Cheng Huang Chiung-An Chen He-Sheng Chou Ya-Yun Huang Wei-Chi Lin Tzu-Chien Li Jia-Jun Yuan Patricia Angela R. Abu Wei-Yuan Chiang Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs IEEE Access Biomedical image panoramic image histogram equalization flat-field correction tooth segmentation tooth position |
| title | Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs |
| title_full | Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs |
| title_fullStr | Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs |
| title_full_unstemmed | Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs |
| title_short | Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs |
| title_sort | missing teeth and restoration detection using dental panoramic radiography based on transfer learning with cnns |
| topic | Biomedical image panoramic image histogram equalization flat-field correction tooth segmentation tooth position |
| url | https://ieeexplore.ieee.org/document/9940956/ |
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