COVID-19-affected medical image analysis using DenserNet

Adak, C; Ghosh, D; Chowdhury, RR; Chattopadhyay, S

COVID-19-affected medical image analysis using DenserNet

Adak, C

Ghosh, D Chowdhury, RR Chattopadhyay, S

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Publisher:: Elsevier
Publication Type:: Chapter
Citation:: Data Science for COVID-19 Volume 1: Computational Perspectives, 2021, pp. 213-230
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.contributor.author	Ghosh, D
dc.contributor.author	Chowdhury, RR
dc.contributor.author	Chattopadhyay, S
dc.date.accessioned	2022-05-11T20:29:17Z
dc.date.available	2022-05-11T20:29:17Z
dc.date.issued	2021-01-01
dc.identifier.citation	Data Science for COVID-19 Volume 1: Computational Perspectives, 2021, pp. 213-230
dc.identifier.isbn	9780128245361
dc.identifier.uri	http://hdl.handle.net/10453/157245
dc.description.abstract	The COrona VIrus Disease (COVID-19) outbreak has been announced as a pandemic by the World Health Organization (WHO) in mid-February 2020. With the current pandemic situation, the testing and detection of this disease are becoming a challenge in many regions across the globe because of the insufficiency of the suitable testing infrastructure. The shortage of kits to test COVID-19 has led to another crisis owing to worldwide supply-demand mismatch, and thereby, widen up a new research area that deals with the detection of COVID-19 without the test kit. In this paper, we investigate medical images, mostly chest X-ray images and thorax computed tomography (CT) scans to identify the attack of COVID-19. In countries, where the number of medical experts is lesser than the expected as recommended by WHO, this computer-aided system can be useful as it requires minimal human intervention. Consequently, this technology reduces the chances of contagious infection. This study may further help in the early detection of people with some similar symptoms of coronavirus. Early detection and intervention can play a pivotal role in coronavirus treatment. The primary goal of our work is to detect COVID-19-affected cases. However, this work can be extended to detect pneumonia because of Severe Acute Respiratory Syndrome, Acute Respiratory Distress Syndrome, Middle East Respiratory Syndrome, and bacteria-like Streptococcus. In this paper, we employ publicly available medical images obtained from various demographics, and propose a rapid cost-effective test leveraging a deep learning-based framework. Here, we propose a new architecture based on a densely connected convolutional neural network to analyze the COVID-19-affected medical images. We name our proposed architecture as DenserNet, which is an improvisation of DenseNet. Our proposed Denser Net architecture achieved 96.18% and 87.19% accuracies on two publicly available databases containing chest X-ray images and thorax CT scans, respectively, for the task of separating COVID-19 and non-COVID-19 images, which is quite encouraging.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Data Science for COVID-19 Volume 1: Computational Perspectives
dc.relation.isbasedon	10.1016/B978-0-12-824536-1.00021-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	COVID-19-affected medical image analysis using DenserNet
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-11T20:29:15Z
pubs.publication-status	Published

Abstract:

The COrona VIrus Disease (COVID-19) outbreak has been announced as a pandemic by the World Health Organization (WHO) in mid-February 2020. With the current pandemic situation, the testing and detection of this disease are becoming a challenge in many regions across the globe because of the insufficiency of the suitable testing infrastructure. The shortage of kits to test COVID-19 has led to another crisis owing to worldwide supply-demand mismatch, and thereby, widen up a new research area that deals with the detection of COVID-19 without the test kit. In this paper, we investigate medical images, mostly chest X-ray images and thorax computed tomography (CT) scans to identify the attack of COVID-19. In countries, where the number of medical experts is lesser than the expected as recommended by WHO, this computer-aided system can be useful as it requires minimal human intervention. Consequently, this technology reduces the chances of contagious infection. This study may further help in the early detection of people with some similar symptoms of coronavirus. Early detection and intervention can play a pivotal role in coronavirus treatment. The primary goal of our work is to detect COVID-19-affected cases. However, this work can be extended to detect pneumonia because of Severe Acute Respiratory Syndrome, Acute Respiratory Distress Syndrome, Middle East Respiratory Syndrome, and bacteria-like Streptococcus. In this paper, we employ publicly available medical images obtained from various demographics, and propose a rapid cost-effective test leveraging a deep learning-based framework. Here, we propose a new architecture based on a densely connected convolutional neural network to analyze the COVID-19-affected medical images. We name our proposed architecture as DenserNet, which is an improvisation of DenseNet. Our proposed Denser Net architecture achieved 96.18% and 87.19% accuracies on two publicly available databases containing chest X-ray images and thorax CT scans, respectively, for the task of separating COVID-19 and non-COVID-19 images, which is quite encouraging.

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http://hdl.handle.net/10453/157245