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...
Ausführliche Beschreibung
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: |
---|
Übergeordnetes Werk: |
In: Electronics - MDPI AG, 2013, 12(2023), 5, p 1167 |
---|---|
Übergeordnetes Werk: |
volume:12 ; year:2023 ; number:5, p 1167 |
Links: |
---|
DOI / URN: |
10.3390/electronics12051167 |
---|
Katalog-ID: |
DOAJ088025977 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ088025977 | ||
003 | DE-627 | ||
005 | 20240413054732.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230410s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/electronics12051167 |2 doi | |
035 | |a (DE-627)DOAJ088025977 | ||
035 | |a (DE-599)DOAJ18ffda872ebd4f2b885a087e343f4b6f | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TK7800-8360 | |
100 | 0 | |a Tao Zhou |e verfasserin |4 aut | |
245 | 1 | 2 | |a A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a 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. | ||
650 | 4 | |a deep learning | |
650 | 4 | |a COVID-19 datasets | |
650 | 4 | |a supervised learning | |
650 | 4 | |a semi-supervised learning | |
650 | 4 | |a unsupervised learning | |
653 | 0 | |a Electronics | |
700 | 0 | |a Fengzhen Liu |e verfasserin |4 aut | |
700 | 0 | |a Huiling Lu |e verfasserin |4 aut | |
700 | 0 | |a Caiyue Peng |e verfasserin |4 aut | |
700 | 0 | |a Xinyu Ye |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Electronics |d MDPI AG, 2013 |g 12(2023), 5, p 1167 |w (DE-627)718626478 |w (DE-600)2662127-7 |x 20799292 |7 nnns |
773 | 1 | 8 | |g volume:12 |g year:2023 |g number:5, p 1167 |
856 | 4 | 0 | |u https://doi.org/10.3390/electronics12051167 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/18ffda872ebd4f2b885a087e343f4b6f |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2079-9292/12/5/1167 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2079-9292 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 12 |j 2023 |e 5, p 1167 |
author_variant |
t z tz f l fl h l hl c p cp x y xy |
---|---|
matchkey_str |
article:20799292:2023----::rveodelanniaigigotc |
hierarchy_sort_str |
2023 |
callnumber-subject-code |
TK |
publishDate |
2023 |
allfields |
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 |
spelling |
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 |
allfields_unstemmed |
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 |
allfieldsGer |
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 |
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 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
deep learning COVID-19 datasets supervised learning semi-supervised learning unsupervised learning Electronics |
isfreeaccess_bool |
true |
container_title |
Electronics |
authorswithroles_txt_mv |
Tao Zhou @@aut@@ Fengzhen Liu @@aut@@ Huiling Lu @@aut@@ Caiyue Peng @@aut@@ Xinyu Ye @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
718626478 |
id |
DOAJ088025977 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ088025977</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413054732.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230410s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/electronics12051167</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ088025977</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ18ffda872ebd4f2b885a087e343f4b6f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK7800-8360</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Tao Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Review of Deep Learning Imaging Diagnostic Methods for COVID-19</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19 datasets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semi-supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unsupervised learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fengzhen Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Huiling Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Caiyue Peng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinyu Ye</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Electronics</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">12(2023), 5, p 1167</subfield><subfield code="w">(DE-627)718626478</subfield><subfield code="w">(DE-600)2662127-7</subfield><subfield code="x">20799292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:5, p 1167</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/electronics12051167</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/18ffda872ebd4f2b885a087e343f4b6f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2079-9292/12/5/1167</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2079-9292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2023</subfield><subfield code="e">5, p 1167</subfield></datafield></record></collection>
|
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 |
authorStr |
Tao Zhou |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)718626478 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TK7800-8360 |
illustrated |
Not Illustrated |
issn |
20799292 |
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 |
topic_unstemmed |
misc TK7800-8360 misc deep learning misc COVID-19 datasets misc supervised learning misc semi-supervised learning misc unsupervised learning misc Electronics |
topic_browse |
misc TK7800-8360 misc deep learning misc COVID-19 datasets misc supervised learning misc semi-supervised learning misc unsupervised learning misc Electronics |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Electronics |
hierarchy_parent_id |
718626478 |
hierarchy_top_title |
Electronics |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)718626478 (DE-600)2662127-7 |
title |
A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 |
ctrlnum |
(DE-627)DOAJ088025977 (DE-599)DOAJ18ffda872ebd4f2b885a087e343f4b6f |
title_full |
A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 |
author_sort |
Tao Zhou |
journal |
Electronics |
journalStr |
Electronics |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
author_browse |
Tao Zhou Fengzhen Liu Huiling Lu Caiyue Peng Xinyu Ye |
container_volume |
12 |
class |
TK7800-8360 |
format_se |
Elektronische Aufsätze |
author-letter |
Tao Zhou |
doi_str_mv |
10.3390/electronics12051167 |
author2-role |
verfasserin |
title_sort |
review of deep learning imaging diagnostic methods for covid-19 |
callnumber |
TK7800-8360 |
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. |
collection_details |
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 |
container_issue |
5, p 1167 |
title_short |
A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 |
url |
https://doi.org/10.3390/electronics12051167 https://doaj.org/article/18ffda872ebd4f2b885a087e343f4b6f https://www.mdpi.com/2079-9292/12/5/1167 https://doaj.org/toc/2079-9292 |
remote_bool |
true |
author2 |
Fengzhen Liu Huiling Lu Caiyue Peng Xinyu Ye |
author2Str |
Fengzhen Liu Huiling Lu Caiyue Peng Xinyu Ye |
ppnlink |
718626478 |
callnumber-subject |
TK - Electrical and Nuclear Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/electronics12051167 |
callnumber-a |
TK7800-8360 |
up_date |
2024-07-03T15:21:54.921Z |
_version_ |
1803571812024451072 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ088025977</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413054732.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230410s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/electronics12051167</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ088025977</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ18ffda872ebd4f2b885a087e343f4b6f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK7800-8360</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Tao Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Review of Deep Learning Imaging Diagnostic Methods for COVID-19</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19 datasets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semi-supervised learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unsupervised learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fengzhen Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Huiling Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Caiyue Peng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinyu Ye</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Electronics</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">12(2023), 5, p 1167</subfield><subfield code="w">(DE-627)718626478</subfield><subfield code="w">(DE-600)2662127-7</subfield><subfield code="x">20799292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:5, p 1167</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/electronics12051167</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/18ffda872ebd4f2b885a087e343f4b6f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2079-9292/12/5/1167</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2079-9292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2023</subfield><subfield code="e">5, p 1167</subfield></datafield></record></collection>
|
score |
7.400218 |