Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shi, Feng | - |
dc.contributor.author | Xia, Liming | - |
dc.contributor.author | Shan, Fei | - |
dc.contributor.author | Song, Bin | - |
dc.contributor.author | Wu, Dijia | - |
dc.contributor.author | Wei, Ying | - |
dc.contributor.author | Yuan, Huan | - |
dc.contributor.author | Jiang, Huiting | - |
dc.contributor.author | He, Yichu | - |
dc.contributor.author | Gao, Yaozong | - |
dc.contributor.author | Sui, He | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-11-22T21:40:16Z | - |
dc.date.available | 2021-11-22T21:40:16Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021-03-21 | - |
dc.identifier.issn | 0031-9155 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/128398 | - |
dc.description.abstract | The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOP PUBLISHING LTD | - |
dc.title | Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1088/1361-6560/abe838 | - |
dc.identifier.scopusid | 2-s2.0-85102731633 | - |
dc.identifier.wosid | 000629942700001 | - |
dc.identifier.bibliographicCitation | PHYSICS IN MEDICINE AND BIOLOGY, v.66, no.6 | - |
dc.relation.isPartOf | PHYSICS IN MEDICINE AND BIOLOGY | - |
dc.citation.title | PHYSICS IN MEDICINE AND BIOLOGY | - |
dc.citation.volume | 66 | - |
dc.citation.number | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | ACUTE RESPIRATORY SYNDROME | - |
dc.subject.keywordPlus | THIN-SECTION CT | - |
dc.subject.keywordPlus | CHEST CT | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | SHRINKAGE | - |
dc.subject.keywordPlus | WUHAN | - |
dc.subject.keywordPlus | CHINA | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | pneumonia | - |
dc.subject.keywordAuthor | decision tree | - |
dc.subject.keywordAuthor | size-aware | - |
dc.subject.keywordAuthor | random forest | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.