A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19) Ahmadian, S, Jalali, Seyed Mohammad Jafar, Islam, SMS, Khosravi, Abbas, Fazli, E and Nahavandi, Saeid 2021, A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19), Computers in Biology and Medicine, vol. 139, pp. 1-14, doi: 10.1016/j.compbiomed.2021.104994. Attached Files Name Description MIMEType Size Downloads Title A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19) Author(s) Ahmadian, S Jalali, Seyed Mohammad Jafar orcid.org/0000-0003-3565-2001 Islam, SMS Khosravi, Abbas orcid.org/0000-0001-6927-0744 Fazli, E Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Computers in Biology and Medicine Volume number 139 Article ID 104994 Start page 1 End page 14 Total pages 14 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021-12 ISSN 0010-4825 1879-0534 Keyword(s) Biology Computer Science Computer Science, Interdisciplinary Applications Convolutional neural network COVID-19 diagnosis Engineering Engineering, Biomedical Evolutionary computation Improved salp swarm algorithm Life Sciences & Biomedicine Life Sciences & Biomedicine - Other Topics Mathematical & Computational Biology RECOMMENDATION METHOD SALP SWARM ALGORITHM Science & Technology Technology Language eng DOI 10.1016/j.compbiomed.2021.104994 Field of Research 08 Information and Computing Sciences 09 Engineering 11 Medical and Health Sciences HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30158672 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 19 Nov 2021, 07:18:27 EST Fri, 19 Nov 2021, 14:44:12 EST Fri, 19 Nov 2021, 14:48:30 EST Sat, 22 Jan 2022, 01:30:14 EST Wed, 02 Feb 2022, 05:03:54 EST Fri, 25 Feb 2022, 13:15:58 EST Fri, 25 Feb 2022, 14:09:09 EST Fri, 25 Feb 2022, 14:13:44 EST Fri, 25 Feb 2022, 14:17:06 EST Sat, 12 Mar 2022, 14:36:16 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 71 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 19 Nov 2021, 07:18:27 EST