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A Review of Deep Learning Imaging Diagnostic Methods for COVID-19.
- Source :
- Electronics (2079-9292); Mar2023, Vol. 12 Issue 5, p1167, 22p
- Publication Year :
- 2023
-
Abstract
- COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 12
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Electronics (2079-9292)
- Publication Type :
- Academic Journal
- Accession number :
- 162348486
- Full Text :
- https://doi.org/10.3390/electronics12051167