UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.
COVID-19
Deep learning
Early fusion
Feature fusion
Uncertainty quantification
Journal
An international journal on information fusion
ISSN: 1566-2535
Titre abrégé: Inf Fusion
Pays: Netherlands
ID NLM: 101513488
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
22
07
2022
revised:
23
09
2022
accepted:
25
09
2022
pubmed:
12
10
2022
medline:
12
10
2022
entrez:
11
10
2022
Statut:
ppublish
Résumé
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called
Identifiants
pubmed: 36217534
doi: 10.1016/j.inffus.2022.09.023
pii: S1566-2535(22)00160-9
pmc: PMC9534540
doi:
Types de publication
Journal Article
Langues
eng
Pagination
364-381Informations de copyright
© 2022 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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