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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Main Authors: Chen, Shih-Lun, Chen, Tsung-Yi, Huang, Yen-Cheng, Chen, Chiung-An, Chou, He-Sheng, Huang, Ya-Yun, Lin, Wei-Chi, Li, Tzu-Chien, Yuan, Jia-Jun, Abu, Patricia Angela R
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Published: Archīum Ateneo 2022
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CNN
Online Access:https://archium.ateneo.edu/discs-faculty-pubs/351
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1351&context=discs-faculty-pubs
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spelling ph-ateneo-arc.discs-faculty-pubs-13512023-01-17T03:16:23Z Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs Chen, Shih-Lun Chen, Tsung-Yi Huang, Yen-Cheng Chen, Chiung-An Chou, He-Sheng Huang, Ya-Yun Lin, Wei-Chi Li, Tzu-Chien Yuan, Jia-Jun Abu, Patricia Angela R 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. 2022-11-07T08:00:00Z text application/pdf https://archium.ateneo.edu/discs-faculty-pubs/351 https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1351&context=discs-faculty-pubs Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Biomedical image panoramic image histogram equalization flat-field correction tooth segmentation tooth position CNN transfer learning Alexnet GoogLeNet Squeezenet Computer Sciences Dentistry Medicine and Health Sciences Physical Sciences and Mathematics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Biomedical image
panoramic image
histogram equalization
flat-field correction
tooth segmentation
tooth position
CNN
transfer learning
Alexnet
GoogLeNet
Squeezenet
Computer Sciences
Dentistry
Medicine and Health Sciences
Physical Sciences and Mathematics
spellingShingle Biomedical image
panoramic image
histogram equalization
flat-field correction
tooth segmentation
tooth position
CNN
transfer learning
Alexnet
GoogLeNet
Squeezenet
Computer Sciences
Dentistry
Medicine and Health Sciences
Physical Sciences and Mathematics
Chen, Shih-Lun
Chen, Tsung-Yi
Huang, Yen-Cheng
Chen, Chiung-An
Chou, He-Sheng
Huang, Ya-Yun
Lin, Wei-Chi
Li, Tzu-Chien
Yuan, Jia-Jun
Abu, Patricia Angela R
Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs
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.
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author Chen, Shih-Lun
Chen, Tsung-Yi
Huang, Yen-Cheng
Chen, Chiung-An
Chou, He-Sheng
Huang, Ya-Yun
Lin, Wei-Chi
Li, Tzu-Chien
Yuan, Jia-Jun
Abu, Patricia Angela R
author_facet Chen, Shih-Lun
Chen, Tsung-Yi
Huang, Yen-Cheng
Chen, Chiung-An
Chou, He-Sheng
Huang, Ya-Yun
Lin, Wei-Chi
Li, Tzu-Chien
Yuan, Jia-Jun
Abu, Patricia Angela R
author_sort Chen, Shih-Lun
title 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_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_sort missing teeth and restoration detection using dental panoramic radiography based on transfer learning with cnns
publisher Archīum Ateneo
publishDate 2022
url https://archium.ateneo.edu/discs-faculty-pubs/351
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1351&context=discs-faculty-pubs
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