A Review of Deep Learning Imaging Diagnostic Methods for COVID-19

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 da...
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Autor*in:	Tao Zhou [verfasserIn] Fengzhen Liu [verfasserIn] Huiling Lu [verfasserIn] Caiyue Peng [verfasserIn] Xinyu Ye [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	deep learning COVID-19 datasets supervised learning semi-supervised learning unsupervised learning

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 5, p 1167
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:5, p 1167

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12051167

Katalog-ID:	DOAJ088025977

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allfieldsSound	10.3390/electronics12051167 doi (DE-627)DOAJ088025977 (DE-599)DOAJ18ffda872ebd4f2b885a087e343f4b6f DE-627 ger DE-627 rakwb eng TK7800-8360 Tao Zhou verfasserin aut A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. deep learning COVID-19 datasets supervised learning semi-supervised learning unsupervised learning Electronics Fengzhen Liu verfasserin aut Huiling Lu verfasserin aut Caiyue Peng verfasserin aut Xinyu Ye verfasserin aut In Electronics MDPI AG, 2013 12(2023), 5, p 1167 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:5, p 1167 https://doi.org/10.3390/electronics12051167 kostenfrei https://doaj.org/article/18ffda872ebd4f2b885a087e343f4b6f kostenfrei https://www.mdpi.com/2079-9292/12/5/1167 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 1167
language	English
source	In Electronics 12(2023), 5, p 1167 volume:12 year:2023 number:5, p 1167
sourceStr	In Electronics 12(2023), 5, p 1167 volume:12 year:2023 number:5, p 1167
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topic_facet	deep learning COVID-19 datasets supervised learning semi-supervised learning unsupervised learning Electronics
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authorswithroles_txt_mv	Tao Zhou @@aut@@ Fengzhen Liu @@aut@@ Huiling Lu @@aut@@ Caiyue Peng @@aut@@ Xinyu Ye @@aut@@
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callnumber-first	T - Technology
author	Tao Zhou
spellingShingle	Tao Zhou misc TK7800-8360 misc deep learning misc COVID-19 datasets misc supervised learning misc semi-supervised learning misc unsupervised learning misc Electronics A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
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topic_title	TK7800-8360 A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 deep learning COVID-19 datasets supervised learning semi-supervised learning unsupervised learning
topic	misc TK7800-8360 misc deep learning misc COVID-19 datasets misc supervised learning misc semi-supervised learning misc unsupervised learning misc Electronics
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title	A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
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title_full	A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
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title_sort	review of deep learning imaging diagnostic methods for covid-19
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title_auth	A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
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.
abstractGer	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_unstemmed	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.
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