COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
Article
Article Title | COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings |
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ERA Journal ID | 16102 |
Article Category | Article |
Authors | Ardakani, Ali Abbasian, Acharya, U. Rajendra, Habibollahi, Sina and Mohammadi, Afshin |
Journal Title | European Radiology |
Journal Citation | 31 (1), pp. 121-130 |
Number of Pages | 10 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 0938-7994 |
1432-1084 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00330-020-07087-y |
Web Address (URL) | https://link.springer.com/article/10.1007/s00330-020-07087-y |
Abstract | Objectives Methods Results Conclusions |
Keywords | Artificial intelligence; COVID-19; Machine learning ; Pneumonia; Tomography; X-ray computed |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Iran University of Medical Sciences, Iran |
Ngee Ann Polytechnic, Singapore | |
Taylor’s University, Malaysia | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore | |
Azad University, Iran | |
Urmia University of Medical Science, Iran |
https://research.usq.edu.au/item/z1v42/covidiag-a-clinical-cad-system-to-diagnose-covid-19-pneumonia-based-on-ct-findings
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