ID | 60283 |
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Sukegawa, Shintaro
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Yoshii, Kazumasa
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Hara, Takeshi
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yamashita, Katsusuke
Polytechnic Center Kagawa
Nakano, Keisuke
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Yamamoto, Norio
Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital
Nagatsuka, Hitoshi
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Furuki, Yoshihiko
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Abstract | In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.
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Keywords | dental implant
artificial intelligence
classification
deep learning
convolutional neural networks
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Published Date | 2020-07-01
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Publication Title |
Biomolecules
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Volume | volume10
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Issue | issue7
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Publisher | MDPI
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Start Page | 984
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ISSN | 2218-273X
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © 2020 by the authors.
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File Version | publisher
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Related Url | isVersionOf https://doi.org/10.3390/biom10070984
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License | http://creativecommons.org/licenses/by/4.0/
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Funder Name |
Japan Society for the Promotion of Science
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助成番号 | 19K19158
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