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 |
|---|---|
| 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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