COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?

Saqib, M; Anwar, A; Anwar, S; Petersson, L; Sharma, N; Blumenstein, M

COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?

Saqib, M

Anwar, A Anwar, S Petersson, L Sharma, N

Blumenstein, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Signals, 3, (2), pp. 296-312

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Field	Value	Language
dc.contributor.author	Saqib, M https://orcid.org/0000-0003-4374-0888
dc.contributor.author	Anwar, A
dc.contributor.author	Anwar, S
dc.contributor.author	Petersson, L
dc.contributor.author	Sharma, N https://orcid.org/0000-0003-0841-1245
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2023-03-21T02:40:48Z
dc.date.available	2023-03-21T02:40:48Z
dc.identifier.citation	Signals, 3, (2), pp. 296-312
dc.identifier.issn	2524-4450
dc.identifier.issn	2624-6120
dc.identifier.uri	http://hdl.handle.net/10453/167913
dc.description.abstract	<jats:p>Deep learning in the last decade has been very successful in computer vision and machine learning applications. Deep learning networks provide state-of-the-art performance in almost all of the applications where they have been employed. In this review, we aim to summarize the essential deep learning techniques and then apply them to COVID-19, a highly contagious viral infection that wreaks havoc on everyone’s lives in various ways. According to the World Health Organization and scientists, more testing potentially helps contain the virus’s spread. The use of chest radiographs is one of the early screening tests for determining disease, as the infection affects the lungs severely. To detect the COVID-19 infection, this experimental survey investigates and automates the process of testing by employing state-of-the-art deep learning classifiers. Moreover, the viruses are of many types, such as influenza, hepatitis, and COVID. Here, our focus is on COVID-19. Therefore, we employ binary classification, where one class is COVID-19 while the other viral infection types are treated as non-COVID-19 in the radiographs. The classification task is challenging due to the limited number of scans available for COVID-19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately and accurately. We train and evaluate 34 models. We also provide the limitations and future direction.</jats:p>
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Signals
dc.relation.isbasedon	10.3390/signals3020019
dc.rights	info:eu-repo/semantics/openAccess
dc.title	COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
dc.type	Journal Article
utslib.citation.volume	3
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T02:40:47Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	3
utslib.citation.issue	2

Abstract:

Deep learning in the last decade has been very successful in computer vision and machine learning applications. Deep learning networks provide state-of-the-art performance in almost all of the applications where they have been employed. In this review, we aim to summarize the essential deep learning techniques and then apply them to COVID-19, a highly contagious viral infection that wreaks havoc on everyone’s lives in various ways. According to the World Health Organization and scientists, more testing potentially helps contain the virus’s spread. The use of chest radiographs is one of the early screening tests for determining disease, as the infection affects the lungs severely. To detect the COVID-19 infection, this experimental survey investigates and automates the process of testing by employing state-of-the-art deep learning classifiers. Moreover, the viruses are of many types, such as influenza, hepatitis, and COVID. Here, our focus is on COVID-19. Therefore, we employ binary classification, where one class is COVID-19 while the other viral infection types are treated as non-COVID-19 in the radiographs. The classification task is challenging due to the limited number of scans available for COVID-19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately and accurately. We train and evaluate 34 models. We also provide the limitations and future direction.

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http://hdl.handle.net/10453/167913