BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification Abdar, Moloud, Fahami, MA, Chakrabarti, S, Khosravi, Abbas, Pławiak, P, Acharya, UR, Tadeusiewicz, R and Nahavandi, Saeid 2021, BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification, Information Sciences, vol. 577, pp. 353-378, doi: 10.1016/j.ins.2021.07.024. Attached Files Name Description MIMEType Size Downloads Title BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification Author(s) Abdar, Moloud orcid.org/0000-0002-3059-6357 Fahami, MA Chakrabarti, S Khosravi, Abbas orcid.org/0000-0001-6927-0744 Pławiak, P Acharya, UR Tadeusiewicz, R Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Information Sciences Volume number 577 Start page 353 End page 378 Total pages 26 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021 ISSN 0020-0255 1872-6291 Keyword(s) Computer Science Computer Science, Information Systems COVID-19 CLASSIFICATION DEEP Deep learning Early fusion Fusion model Medical image classification Monte Carlo dropout Science & Technology Technology Uncertainty quantification Language eng DOI 10.1016/j.ins.2021.07.024 Field of Research 01 Mathematical Sciences 08 Information and Computing Sciences 09 Engineering HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30153976 Document type: Journal Article Collection: Institute for Intelligent Systems Research and Innovation (IISRI) Related Links Link Description 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. Versions Version Filter Type Fri, 30 Jul 2021, 08:24:29 EST Sat, 31 Jul 2021, 05:00:28 EST Mon, 02 Aug 2021, 08:08:10 EST Sat, 16 Oct 2021, 01:30:44 EST Wed, 24 Nov 2021, 05:02:35 EST Wed, 12 Jan 2022, 13:43:03 EST Wed, 12 Jan 2022, 15:42:07 EST Wed, 12 Jan 2022, 15:46:20 EST Filtered Full Citation counts:   Cited 10 times in TR Web of Science Cited 14 times in Scopus Search Google Scholar Access Statistics: 49 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 30 Jul 2021, 08:24:29 EST