- Title
- Deep learning and federated learning for screening COVID-19 : a review
- Creator
- Mondal, M.; Bharati, Subrato; Podder, Prajoy; Kamruzzaman, Joarder
- Date
- 2023
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197658
- Identifier
- vital:18918
- Identifier
-
https://doi.org/10.3390/biomedinformatics3030045
- Identifier
- ISSN:2673-7426 (ISSN)
- Abstract
- Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. © 2023 by the authors.
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Relation
- BioMedInformatics Vol. 3, no. 3 (2023), p. 691-713
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 32 Biomedical and clinical sciences; Computed tomography; Coronavirus; Covid-19; Deep learning; Densenet; Federated learning
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