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- Publisher Website: 10.1109/ACCESS.2018.2843392
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Article: Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks
Title | Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks |
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Authors | |
Keywords | Classification Convolutional neural network Detection Radiographs Skeletal maturity |
Issue Date | 2018 |
Publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 |
Citation | IEEE Access, 2018, v. 6, p. 29979-29993 How to Cite? |
Abstract | In this paper, we present an automated skeletal maturity recognition system that takes a single hand radiograph as an input and finally output the bone age prediction. Unlike the conventional manually diagnostic methods, which are laborious, fallible, and time-consuming, the proposed system takes input images and generates classification results directly. It first accurately detects the distal radius and ulna areas from the hand and wrist X-ray images by a faster region-based convolutional neural network (CNN) model. Then, a well-tuned CNN classification model is applied to estimate the bone ages. In the experiment section, we employed a data set of 1101 hand and wrist radiographs and conducted comprehensive experiments on the proposed system. We discussed the model performance according to various network configurations, multiple optimization algorithms, and different training sample amounts. After parameter optimization, the proposed model is finally achieved 92% and 90% classification accuracies for radius and ulna grades, respectively. |
Persistent Identifier | http://hdl.handle.net/10722/259414 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 0.960 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, SQ | - |
dc.contributor.author | Shen, YY | - |
dc.contributor.author | Shi, CH | - |
dc.contributor.author | Yin, P | - |
dc.contributor.author | Wang, ZH | - |
dc.contributor.author | Cheung, WHP | - |
dc.contributor.author | Cheung, JPY | - |
dc.contributor.author | Luk, KDK | - |
dc.contributor.author | Hu, Y | - |
dc.date.accessioned | 2018-09-03T04:07:03Z | - |
dc.date.available | 2018-09-03T04:07:03Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Access, 2018, v. 6, p. 29979-29993 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259414 | - |
dc.description.abstract | In this paper, we present an automated skeletal maturity recognition system that takes a single hand radiograph as an input and finally output the bone age prediction. Unlike the conventional manually diagnostic methods, which are laborious, fallible, and time-consuming, the proposed system takes input images and generates classification results directly. It first accurately detects the distal radius and ulna areas from the hand and wrist X-ray images by a faster region-based convolutional neural network (CNN) model. Then, a well-tuned CNN classification model is applied to estimate the bone ages. In the experiment section, we employed a data set of 1101 hand and wrist radiographs and conducted comprehensive experiments on the proposed system. We discussed the model performance according to various network configurations, multiple optimization algorithms, and different training sample amounts. After parameter optimization, the proposed model is finally achieved 92% and 90% classification accuracies for radius and ulna grades, respectively. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 | - |
dc.relation.ispartof | IEEE Access | - |
dc.rights | © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. | - |
dc.subject | Classification | - |
dc.subject | Convolutional neural network | - |
dc.subject | Detection | - |
dc.subject | Radiographs | - |
dc.subject | Skeletal maturity | - |
dc.title | Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks | - |
dc.type | Article | - |
dc.identifier.email | Cheung, WHP: gnuehcp6@hku.hk | - |
dc.identifier.email | Cheung, JPY: cheungjp@hku.hk | - |
dc.identifier.email | Luk, KDK: hrmoldk@HKUCC-COM.hku.hk | - |
dc.identifier.email | Hu, Y: yhud@hku.hk | - |
dc.identifier.authority | Cheung, JPY=rp01685 | - |
dc.identifier.authority | Luk, KDK=rp00333 | - |
dc.identifier.authority | Hu, Y=rp00432 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2843392 | - |
dc.identifier.scopus | eid_2-s2.0-85048021528 | - |
dc.identifier.hkuros | 288735 | - |
dc.identifier.volume | 6 | - |
dc.identifier.spage | 29979 | - |
dc.identifier.epage | 29993 | - |
dc.identifier.isi | WOS:000435522600045 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2169-3536 | - |