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CNN Based Covid-19 Detection from Image Processing

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 1 (2023) · pp. 99–113

DOI
10.5614/itbj.ict.res.appl.2023.17.1.7
ISSN
2338-5499, 2337-5787
Published
2023-05-31
Publisher
DRI - ITB
Language
eng

Abstract

Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.

Keywords

Collaboration network of the authors

4 authors · 6 co-authorship links · 3 across different organisations

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  • American International University-Bangladesh
  • University of South Asia
  • University of Liberal Arts Bangladesh
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CNN Based Covid-19 Detection from Image Processing</dc:title>
	<dc:creator>Rahman, Mohammed Ashikur</dc:creator>
	<dc:creator>Islam, Mohammad Rabiul </dc:creator>
	<dc:creator>Rafath, Md. Anzir Hossain</dc:creator>
	<dc:creator>Mhejabin, Simron </dc:creator>
	<dc:subject xml:lang="en-US">Covid-19 detection</dc:subject>
	<dc:subject xml:lang="en-US">CNN</dc:subject>
	<dc:subject xml:lang="en-US">DenseNet</dc:subject>
	<dc:subject xml:lang="en-US">image processing</dc:subject>
	<dc:subject xml:lang="en-US">pneumonia detection</dc:subject>
	<dc:description xml:lang="en-US">Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-05-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17542</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.17.1.7</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 1 (2023); 99-113</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.17.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17542/6212</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of ICT Research and Applications</dc:rights>
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