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Article: Skeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks

TitleSkeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks
Authors
KeywordsClassification
Convolutional neural network
Detection
Radiographs
Skeletal maturity
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. 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?
AbstractIn 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 Identifierhttp://hdl.handle.net/10722/259414
ISSN
2017 Impact Factor: 3.557
2015 SCImago Journal Rankings: 0.947
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, SQ-
dc.contributor.authorShen, YY-
dc.contributor.authorShi, CH-
dc.contributor.authorYin, P-
dc.contributor.authorWang, ZH-
dc.contributor.authorCheung, WHP-
dc.contributor.authorCheung, JPY-
dc.contributor.authorLuk, KDK-
dc.contributor.authorHu, Y-
dc.date.accessioned2018-09-03T04:07:03Z-
dc.date.available2018-09-03T04:07:03Z-
dc.date.issued2018-
dc.identifier.citationIEEE Access, 2018, v. 6, p. 29979-29993-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/259414-
dc.description.abstractIn 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.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE 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.subjectClassification-
dc.subjectConvolutional neural network-
dc.subjectDetection-
dc.subjectRadiographs-
dc.subjectSkeletal maturity-
dc.titleSkeletal Maturity Recognition Using a Fully Automated System With Convolutional Neural Networks-
dc.typeArticle-
dc.identifier.emailCheung, WHP: gnuehcp6@hku.hk-
dc.identifier.emailCheung, JPY: cheungjp@hku.hk-
dc.identifier.emailLuk, KDK: hrmoldk@HKUCC-COM.hku.hk-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityCheung, JPY=rp01685-
dc.identifier.authorityLuk, KDK=rp00333-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2018.2843392-
dc.identifier.scopuseid_2-s2.0-85048021528-
dc.identifier.hkuros288735-
dc.identifier.volume6-
dc.identifier.spage29979-
dc.identifier.epage29993-
dc.identifier.isiWOS:000435522600045-
dc.publisher.placeUnited States-

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