A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists,...

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Main Authors: Chuo, Yueh, Lin, Wen-Ming, Chen, Tsung-Yi, Chan, Mei-Ling, Chang, Yu-Sung, Lin, Yan-Ru, Lin, Yuan-Jin, Shao, Yu-Han, Chen, Chiung-An, Abu, Patricia Angela R
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Published: Archīum Ateneo 2022
Subjects:
PA
CNN
Online Access:https://archium.ateneo.edu/discs-faculty-pubs/355
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1355&context=discs-faculty-pubs
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-13552023-01-24T00:54:23Z A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph Chuo, Yueh Lin, Wen-Ming Chen, Tsung-Yi Chan, Mei-Ling Chang, Yu-Sung Lin, Yan-Ru Lin, Yuan-Jin Shao, Yu-Han Chen, Chiung-An Abu, Patricia Angela R Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0. 2022-12-06T08:00:00Z text application/pdf https://archium.ateneo.edu/discs-faculty-pubs/355 https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1355&context=discs-faculty-pubs Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo PA CNN tooth disease recognition image segmentation image preprocessing Analytical, Diagnostic and Therapeutic Techniques and Equipment Artificial Intelligence and Robotics 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 PA
CNN
tooth disease recognition
image segmentation
image preprocessing
Analytical, Diagnostic and Therapeutic Techniques and Equipment
Artificial Intelligence and Robotics
Computer Sciences
Dentistry
Medicine and Health Sciences
Physical Sciences and Mathematics
spellingShingle PA
CNN
tooth disease recognition
image segmentation
image preprocessing
Analytical, Diagnostic and Therapeutic Techniques and Equipment
Artificial Intelligence and Robotics
Computer Sciences
Dentistry
Medicine and Health Sciences
Physical Sciences and Mathematics
Chuo, Yueh
Lin, Wen-Ming
Chen, Tsung-Yi
Chan, Mei-Ling
Chang, Yu-Sung
Lin, Yan-Ru
Lin, Yuan-Jin
Shao, Yu-Han
Chen, Chiung-An
Abu, Patricia Angela R
A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
description Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.
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author Chuo, Yueh
Lin, Wen-Ming
Chen, Tsung-Yi
Chan, Mei-Ling
Chang, Yu-Sung
Lin, Yan-Ru
Lin, Yuan-Jin
Shao, Yu-Han
Chen, Chiung-An
Abu, Patricia Angela R
author_facet Chuo, Yueh
Lin, Wen-Ming
Chen, Tsung-Yi
Chan, Mei-Ling
Chang, Yu-Sung
Lin, Yan-Ru
Lin, Yuan-Jin
Shao, Yu-Han
Chen, Chiung-An
Abu, Patricia Angela R
author_sort Chuo, Yueh
title A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
title_short A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
title_full A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
title_fullStr A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
title_full_unstemmed A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
title_sort high-accuracy detection system: based on transfer learning for apical lesions on periapical radiograph
publisher Archīum Ateneo
publishDate 2022
url https://archium.ateneo.edu/discs-faculty-pubs/355
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1355&context=discs-faculty-pubs
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