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dc.contributor.authorJavaheri, T.en_US
dc.contributor.authorHomayounfar, M.en_US
dc.contributor.authorAmoozgar, Z.en_US
dc.contributor.authorReiazi, R.en_US
dc.contributor.authorHomayounieh, F.en_US
dc.contributor.authorAbbas, E.en_US
dc.contributor.authorLaali, A.en_US
dc.contributor.authorRadmard, A.R.en_US
dc.contributor.authorGharib, M.H.en_US
dc.contributor.authorMousavi, S.A.J.en_US
dc.contributor.authorGhaemi, O.en_US
dc.contributor.authorBabaei, R.en_US
dc.contributor.authorMobin, H.K.en_US
dc.contributor.authorHosseinzadeh, M.en_US
dc.contributor.authorJahanban-Esfahlan, R.en_US
dc.contributor.authorSeidi, K.en_US
dc.contributor.authorKalra, M.K.en_US
dc.contributor.authorZhang, G.en_US
dc.contributor.authorChitkushev, L.en_US
dc.contributor.authorHaibe-Kains, B.en_US
dc.contributor.authorMalekzadeh, R.en_US
dc.contributor.authorRawassizadeh, R.en_US
dc.date.accessioned2021-09-16T15:43:27Z
dc.date.available2021-09-16T15:43:27Z
dc.date.issued2021-02-17
dc.identifier.citationT. Javaheri, M. Homayounfar, Z. Amoozgar, R. Reiazi, F. Homayounieh, E. Abbas, A. Laali, A.R. Radmard, M.H. Gharib, S.A.J. Mousavi, O. Ghaemi, R. Babaei, H.K. Mobin, M. Hosseinzadeh, R. Jahanban-Esfahlan, K. Seidi, M.K. Kalra, G. Zhang, L. Chitkushev, B. Haibe-Kains, R. Malekzadeh, R. Rawassizadeh. 2021. "CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images." npj Digital Medicine, https://doi.org/10.1038/s41746-021-00399-3
dc.identifier.issn2398-6352
dc.identifier.urihttps://hdl.handle.net/2144/43024
dc.description.abstractCoronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptasepolymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.en_US
dc.publisherNature Research (part of Springer Nature)en_US
dc.relation.ispartofnpj Digital Medicine
dc.rights© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/.
dc.titleCovidCTNet: an open-source deep learning approach to diagnose Covid-19 using small cohort of CT imagesen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1038/s41746-021-00399-3
pubs.elements-sourcemanual-entryen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.organisational-groupBoston University, College of Communicationen_US
pubs.organisational-groupBoston University, College of Communication, COM ADMINISTRATIONen_US
pubs.organisational-groupBoston University, College of Fine Artsen_US
pubs.organisational-groupBoston University, College of Fine Arts, College of Fine Artsen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent Collegeen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent College, Administrationen_US
pubs.organisational-groupBoston University, Metropolitan Collegeen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv614866


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© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.