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Deep Learning and Federated Learning for Screening COVID-19: A Review.
- Source :
-
BioMedInformatics . Sep2023, Vol. 3 Issue 3, p691-713. 23p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 26737426
- Volume :
- 3
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- BioMedInformatics
- Publication Type :
- Academic Journal
- Accession number :
- 172394196
- Full Text :
- https://doi.org/10.3390/biomedinformatics3030045