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An uncertainty-aware transfer learning-based framework for covid-19 diagnosis

Shamsi, A, Asgharnezhad, H, Jokandan, S S, Khosravi, Abbas, Kebria, P M, Nahavandi, D, Nahavandi, Saeid and Srinivasan, D 2021, An uncertainty-aware transfer learning-based framework for covid-19 diagnosis, IEEE transactions on neural networks and learning systems, vol. 32, no. 4, pp. 1408-1417, doi: 10.1109/TNNLS.2021.3054306.

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Title An uncertainty-aware transfer learning-based framework for covid-19 diagnosis
Author(s) Shamsi, A
Asgharnezhad, H
Jokandan, S S
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Kebria, P M
Nahavandi, D
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Srinivasan, D
Journal name IEEE transactions on neural networks and learning systems
Volume number 32
Issue number 4
Start page 1408
End page 1417
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2021-04
ISSN 2162-237X
2162-2388
Keyword(s) Classification
Computed tomography
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
COVID-19
Data models
deep learning
Engineering
Engineering, Electrical & Electronic
Feature extraction
Science & Technology
Technology
Training
transfer learning
Uncertainty
uncertainty quantification
X-ray imaging
Language eng
DOI 10.1109/TNNLS.2021.3054306
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148462

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Created: Mon, 01 Mar 2021, 13:39:14 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.