ID | 64140 |
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Sukegawa, Shintaro
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Yoshii, Kazumasa
Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University
Hara, Takeshi
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Tanaka, Futa
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yamashita, Katsusuke
Polytechnic Center Kagawa
Kagaya, Tutaro
Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University
Nakano, Keisuke
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Takabatake, Kiyofumi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Kawai, Hotaka
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Nagatsuka, Hitoshi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Furuki, Yoshihiko
Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
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Abstract | Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to "Huge" for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.
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Published Date | 2022-07-27
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Publication Title |
Plos One
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Volume | volume17
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Issue | issue7
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Publisher | Public Library Science
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Start Page | e0269016
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ISSN | 1932-6203
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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 | © 2022 Sukegawa et al.
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File Version | publisher
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Related Url | isVersionOf https://doi.org/10.1371/journal.pone.0269016
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License | https://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 | JP19K19158
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