UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.

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Bibliographic Details
Title: UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.
Authors: Abdar M; Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia., Salari S; Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada., Qahremani S; Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran., Lam HK; Centre for Robotics Research, Department of Engineering, King's College London, London, United Kingdom., Karray F; Centre for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.; Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates., Hussain S; System Administrator, Dibrugarh University, Dibrugarh, India., Khosravi A; Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia., Acharya UR; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore.; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan., Makarenkov V; Department of Computer Science, University of Quebec in Montreal, Montreal, Canada., Nahavandi S; Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.
Source: An international journal on information fusion [Inf Fusion] 2023 Feb; Vol. 90, pp. 364-381. Date of Electronic Publication: 2022 Oct 05.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101513488 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1566-2535 (Print) Linking ISSN: 15662535 NLM ISO Abbreviation: Inf Fusion Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1566-2535
DOI:10.1016/j.inffus.2022.09.023