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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Archīum Ateneo
2022
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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 |
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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 |
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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 |
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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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text |
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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