c8war0m6

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?:abstract	Aim:: COVID-19 is a disease caused by a new strain of coronavirus Up to 18th October 2020, worldwide there have been 39 6 million confirmed cases resulting in more than 1 1 million deaths To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images Methods:: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images;and 306 healthy control images We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L) Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models Micro-averaged (MA) F1 score was used as the measuring indicator The final determined model was named CCSHNet Results:: On the test set, CCSHNet achieved sensitivities of four classes of 95 61%, 96 25%, 98 30%, and 97 86%, respectively The precision values of four classes were 97 32%, 96 42%, 96 99%, and 97 38%, respectively The F1 scores of four classes were 96 46%, 96 33%, 97 64%, and 97 62%, respectively The MA F1 score was 97 04% In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods Conclusions:: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs © 2020 Elsevier Ltd • We proposed a novel (L, 2) transfer feature learning (L2TFL) approach • L2TFL can elucidate the optimal layers to be removed prior to selection • We developed a novel selection algorithm of pretrained network for fusion approach • SAPNF can determine the best two pretrained models for fusion • We introduced a deep CCT fusion discriminant correlation analysis fusion method : COVID-19 is a disease caused by a new strain of coronavirus Up to 18th October 2020, worldwide there have been 39 6 million confirmed cases resulting in more than 1 1 million deaths To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images;and 306 healthy control images We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L) Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models Micro-averaged (MA) F1 score was used as the measuring indicator The final determined model was named CCSHNet : On the test set, CCSHNet achieved sensitivities of four classes of 95 61%, 96 25%, 98 30%, and 97 86%, respectively The precision values of four classes were 97 32%, 96 42%, 96 99%, and 97 38%, respectively The F1 scores of four classes were 96 46%, 96 33%, 97 64%, and 97 62%, respectively The MA F1 score was 97 04% In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs [ABSTRACT FROM AUTHOR] Copyright of Information Fusion is the property of Elsevier B V and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder\'s express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
?:creator	<https://research.tib.eu/covid-19/entity/Wang%2C_S._H.%3B_Nayak%2C_D._R.%3B_Guttery%2C_D._S.%3B_Zhang%2C_X.%3B_Zhang%2C_Y._D.> <https://research.tib.eu/covid-19/entity/Wang%2C_Shui-Hua_Nayak_Deepak_Ranjan_Guttery_David_S.%3B_Zhang%2C_Xin_Zhang_Yu-Dong>
?:journal	Information_Fusion
?:license	unk
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
is ?:relation_isRelatedTo_publication of	<https://research.tib.eu/covid-19/entity/c8war0m6_HAS_C0012634_CAUSES_C0206750>
?:source	WHO
?:title	COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:who_covidence_id	#1002649 #953044
?:year	2021

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?:abstract

Aim:: COVID-19 is a disease caused by a new strain of coronavirus Up to 18th October 2020, worldwide there have been 39 6 million confirmed cases resulting in more than 1 1 million deaths To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images Methods:: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images;and 306 healthy control images We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L) Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models Micro-averaged (MA) F1 score was used as the measuring indicator The final determined model was named CCSHNet Results:: On the test set, CCSHNet achieved sensitivities of four classes of 95 61%, 96 25%, 98 30%, and 97 86%, respectively The precision values of four classes were 97 32%, 96 42%, 96 99%, and 97 38%, respectively The F1 scores of four classes were 96 46%, 96 33%, 97 64%, and 97 62%, respectively The MA F1 score was 97 04% In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods Conclusions:: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs © 2020 Elsevier Ltd
• We proposed a novel (L, 2) transfer feature learning (L2TFL) approach • L2TFL can elucidate the optimal layers to be removed prior to selection • We developed a novel selection algorithm of pretrained network for fusion approach • SAPNF can determine the best two pretrained models for fusion • We introduced a deep CCT fusion discriminant correlation analysis fusion method : COVID-19 is a disease caused by a new strain of coronavirus Up to 18th October 2020, worldwide there have been 39 6 million confirmed cases resulting in more than 1 1 million deaths To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images;and 306 healthy control images We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L) Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models Micro-averaged (MA) F1 score was used as the measuring indicator The final determined model was named CCSHNet : On the test set, CCSHNet achieved sensitivities of four classes of 95 61%, 96 25%, 98 30%, and 97 86%, respectively The precision values of four classes were 97 32%, 96 42%, 96 99%, and 97 38%, respectively The F1 scores of four classes were 96 46%, 96 33%, 97 64%, and 97 62%, respectively The MA F1 score was 97 04% In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs [ABSTRACT FROM AUTHOR] Copyright of Information Fusion is the property of Elsevier B V and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder\'s express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

?:creator