Deep Neural Networks for Dental Implant System Classification
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 ra...
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doaj-c17a561a9e424951a6cd1b4254138aed2020-11-25T03:07:21ZengMDPI AGBiomolecules2218-273X2020-07-011098498410.3390/biom10070984Deep Neural Networks for Dental Implant System ClassificationShintaro Sukegawa0Kazumasa Yoshii1Takeshi Hara2Katsusuke Yamashita3Keisuke Nakano4Norio Yamamoto5Hitoshi Nagatsuka6Yoshihiko Furuki7Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, JapanDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, JapanDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, JapanPolytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063, JapanDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, JapanDepartment of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, JapanDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, JapanIn 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.https://www.mdpi.com/2218-273X/10/7/984dental implantartificial intelligenceclassificationdeep learningconvolutional neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Katsusuke Yamashita Keisuke Nakano Norio Yamamoto Hitoshi Nagatsuka Yoshihiko Furuki |
spellingShingle |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Katsusuke Yamashita Keisuke Nakano Norio Yamamoto Hitoshi Nagatsuka Yoshihiko Furuki Deep Neural Networks for Dental Implant System Classification Biomolecules dental implant artificial intelligence classification deep learning convolutional neural networks |
author_facet |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Katsusuke Yamashita Keisuke Nakano Norio Yamamoto Hitoshi Nagatsuka Yoshihiko Furuki |
author_sort |
Shintaro Sukegawa |
title |
Deep Neural Networks for Dental Implant System Classification |
title_short |
Deep Neural Networks for Dental Implant System Classification |
title_full |
Deep Neural Networks for Dental Implant System Classification |
title_fullStr |
Deep Neural Networks for Dental Implant System Classification |
title_full_unstemmed |
Deep Neural Networks for Dental Implant System Classification |
title_sort |
deep neural networks for dental implant system classification |
publisher |
MDPI AG |
series |
Biomolecules |
issn |
2218-273X |
publishDate |
2020-07-01 |
description |
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. |
topic |
dental implant artificial intelligence classification deep learning convolutional neural networks |
url |
https://www.mdpi.com/2218-273X/10/7/984 |
work_keys_str_mv |
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