Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images.

Bargshady, G; Zhou, X; Barua, PD; Gururajan, R; Li, Y; Acharya, UR

Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images.

Bargshady, G Zhou, X Barua, PD Gururajan, R Li, Y Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Pattern Recognit Lett, 2022, 153, pp. 67-74
Issue Date:: 2022-01

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Field	Value	Language
dc.contributor.author	Bargshady, G
dc.contributor.author	Zhou, X
dc.contributor.author	Barua, PD
dc.contributor.author	Gururajan, R
dc.contributor.author	Li, Y
dc.contributor.author	Acharya, UR
dc.date.accessioned	2023-08-23T02:47:06Z
dc.date.available	2021-11-18
dc.date.available	2023-08-23T02:47:06Z
dc.date.issued	2022-01
dc.identifier.citation	Pattern Recognit Lett, 2022, 153, pp. 67-74
dc.identifier.issn	0167-8655
dc.identifier.uri	http://hdl.handle.net/10453/171816
dc.description.abstract	Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Pattern Recognit Lett
dc.relation.isbasedon	10.1016/j.patrec.2021.11.020
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images.
dc.type	Journal Article
utslib.citation.volume	153
utslib.location.activity	Netherlands
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-08-23T02:47:02Z
pubs.publication-status	Published
pubs.volume	153

Abstract:

Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.

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http://hdl.handle.net/10453/171816