Towards computer-aided severity assessment via deep neural networks for geographic and opacity exten...
Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays
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Author / Creator
Wong, A. , Lin, Z. Q. , Wang, L. , Chung, A. G. , Shen, B. , Abbasi, A. , Hoshmand-Kochi, M. and Duong, T. Q.
Publisher
London: Nature Publishing Group UK
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Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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Contents
A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics b...
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Full title
Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays
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TN_cdi_doaj_primary_oai_doaj_org_article_74b7e4bc91ae48dea4e9ec5db294636c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_74b7e4bc91ae48dea4e9ec5db294636c
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-88538-4
