Predicting pattern of coronavirus using X-ray and CT scan images
Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reac...
Gespeichert in:
Veröffentlicht in: | Network modeling and analysis in health informatics and bioinformatics (Wien) 2022-12, Vol.11 (1), p.39, Article 39 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | 39 |
container_title | Network modeling and analysis in health informatics and bioinformatics (Wien) |
container_volume | 11 |
creator | Khurana Batra, Payal Aggarwal, Paras Wadhwa, Dheeraj Gulati, Mehul |
description | Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy. |
doi_str_mv | 10.1007/s13721-022-00382-2 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9532815</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2919735091</sourcerecordid><originalsourceid>FETCH-LOGICAL-c381t-6ba99de4229671cde8c1dd2faaf8e18edd3f4a3c2b8f7f8dbe312336b9e897f53</originalsourceid><addsrcrecordid>eNp9UE1LAzEQDaJgqf0DnhY8ryaT7m5yEaX4BQU9VPAWsvlYt7RJTXYL_fembql4cRiYgXnvzcxD6JLga4JxdRMJrYDkGCDHmDLI4QSNgHDIy7LCp8e-hHM0iXGJU7CUpBihu7dgdKu61jXZRnadCS7zNlM-eCe3behj1sf98CMPcpdJp7PZIotKuqxdy8bEC3Rm5SqayaGO0fvjw2L2nM9fn15m9_NcUUa6vKwl59pMAXhZEaUNU0RrsFJaZggzWlM7lVRBzWxlma4NJUBpWXPDeGULOka3g-6mr9dGK-O6IFdiE9IZYSe8bMXfiWs_ReO3ghcU0qtJ4OogEPxXb2Inlr4PLt0sgBNe0QJzklAwoFTwMQZjjxsIFnu3xeC2SG6LH7cFJBIdSDGBXWPCr_Q_rG_GmYN5</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2919735091</pqid></control><display><type>article</type><title>Predicting pattern of coronavirus using X-ray and CT scan images</title><source>SpringerLink Journals</source><source>ProQuest Central</source><creator>Khurana Batra, Payal ; Aggarwal, Paras ; Wadhwa, Dheeraj ; Gulati, Mehul</creator><creatorcontrib>Khurana Batra, Payal ; Aggarwal, Paras ; Wadhwa, Dheeraj ; Gulati, Mehul</creatorcontrib><description>Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.</description><identifier>ISSN: 2192-6662</identifier><identifier>EISSN: 2192-6670</identifier><identifier>DOI: 10.1007/s13721-022-00382-2</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Accuracy ; Algorithms ; Applications of Graph Theory and Complex Networks ; Applications programs ; Artificial intelligence ; Artificial neural networks ; Automation ; Bioinformatics ; Cardiovascular disease ; Computational Biology/Bioinformatics ; Computed tomography ; Computer Science ; Coronaviruses ; COVID-19 ; Datasets ; Deep learning ; Health Informatics ; Lungs ; Machine learning ; Medical imaging ; Neural networks ; Original ; Original Article ; Patients ; Pneumonia ; Polymerase chain reaction ; Respiratory system ; Reverse transcription ; Sensitivity ; Signs and symptoms ; Viral diseases ; Viruses ; X-rays</subject><ispartof>Network modeling and analysis in health informatics and bioinformatics (Wien), 2022-12, Vol.11 (1), p.39, Article 39</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-6ba99de4229671cde8c1dd2faaf8e18edd3f4a3c2b8f7f8dbe312336b9e897f53</citedby><cites>FETCH-LOGICAL-c381t-6ba99de4229671cde8c1dd2faaf8e18edd3f4a3c2b8f7f8dbe312336b9e897f53</cites><orcidid>0000-0003-1926-288X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13721-022-00382-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919735091?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,776,780,881,21369,27903,27904,33723,41467,42536,43784,51297</link.rule.ids></links><search><creatorcontrib>Khurana Batra, Payal</creatorcontrib><creatorcontrib>Aggarwal, Paras</creatorcontrib><creatorcontrib>Wadhwa, Dheeraj</creatorcontrib><creatorcontrib>Gulati, Mehul</creatorcontrib><title>Predicting pattern of coronavirus using X-ray and CT scan images</title><title>Network modeling and analysis in health informatics and bioinformatics (Wien)</title><addtitle>Netw Model Anal Health Inform Bioinforma</addtitle><description>Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Applications of Graph Theory and Complex Networks</subject><subject>Applications programs</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Bioinformatics</subject><subject>Cardiovascular disease</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computed tomography</subject><subject>Computer Science</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Health Informatics</subject><subject>Lungs</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Original</subject><subject>Original Article</subject><subject>Patients</subject><subject>Pneumonia</subject><subject>Polymerase chain reaction</subject><subject>Respiratory system</subject><subject>Reverse transcription</subject><subject>Sensitivity</subject><subject>Signs and symptoms</subject><subject>Viral diseases</subject><subject>Viruses</subject><subject>X-rays</subject><issn>2192-6662</issn><issn>2192-6670</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9UE1LAzEQDaJgqf0DnhY8ryaT7m5yEaX4BQU9VPAWsvlYt7RJTXYL_fembql4cRiYgXnvzcxD6JLga4JxdRMJrYDkGCDHmDLI4QSNgHDIy7LCp8e-hHM0iXGJU7CUpBihu7dgdKu61jXZRnadCS7zNlM-eCe3behj1sf98CMPcpdJp7PZIotKuqxdy8bEC3Rm5SqayaGO0fvjw2L2nM9fn15m9_NcUUa6vKwl59pMAXhZEaUNU0RrsFJaZggzWlM7lVRBzWxlma4NJUBpWXPDeGULOka3g-6mr9dGK-O6IFdiE9IZYSe8bMXfiWs_ReO3ghcU0qtJ4OogEPxXb2Inlr4PLt0sgBNe0QJzklAwoFTwMQZjjxsIFnu3xeC2SG6LH7cFJBIdSDGBXWPCr_Q_rG_GmYN5</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Khurana Batra, Payal</creator><creator>Aggarwal, Paras</creator><creator>Wadhwa, Dheeraj</creator><creator>Gulati, Mehul</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1926-288X</orcidid></search><sort><creationdate>20221201</creationdate><title>Predicting pattern of coronavirus using X-ray and CT scan images</title><author>Khurana Batra, Payal ; Aggarwal, Paras ; Wadhwa, Dheeraj ; Gulati, Mehul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-6ba99de4229671cde8c1dd2faaf8e18edd3f4a3c2b8f7f8dbe312336b9e897f53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Applications of Graph Theory and Complex Networks</topic><topic>Applications programs</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Automation</topic><topic>Bioinformatics</topic><topic>Cardiovascular disease</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computed tomography</topic><topic>Computer Science</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Health Informatics</topic><topic>Lungs</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Original</topic><topic>Original Article</topic><topic>Patients</topic><topic>Pneumonia</topic><topic>Polymerase chain reaction</topic><topic>Respiratory system</topic><topic>Reverse transcription</topic><topic>Sensitivity</topic><topic>Signs and symptoms</topic><topic>Viral diseases</topic><topic>Viruses</topic><topic>X-rays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khurana Batra, Payal</creatorcontrib><creatorcontrib>Aggarwal, Paras</creatorcontrib><creatorcontrib>Wadhwa, Dheeraj</creatorcontrib><creatorcontrib>Gulati, Mehul</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Network modeling and analysis in health informatics and bioinformatics (Wien)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khurana Batra, Payal</au><au>Aggarwal, Paras</au><au>Wadhwa, Dheeraj</au><au>Gulati, Mehul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting pattern of coronavirus using X-ray and CT scan images</atitle><jtitle>Network modeling and analysis in health informatics and bioinformatics (Wien)</jtitle><stitle>Netw Model Anal Health Inform Bioinforma</stitle><date>2022-12-01</date><risdate>2022</risdate><volume>11</volume><issue>1</issue><spage>39</spage><pages>39-</pages><artnum>39</artnum><issn>2192-6662</issn><eissn>2192-6670</eissn><abstract>Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s13721-022-00382-2</doi><orcidid>https://orcid.org/0000-0003-1926-288X</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2192-6662 |
ispartof | Network modeling and analysis in health informatics and bioinformatics (Wien), 2022-12, Vol.11 (1), p.39, Article 39 |
issn | 2192-6662 2192-6670 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9532815 |
source | SpringerLink Journals; ProQuest Central |
subjects | Accuracy Algorithms Applications of Graph Theory and Complex Networks Applications programs Artificial intelligence Artificial neural networks Automation Bioinformatics Cardiovascular disease Computational Biology/Bioinformatics Computed tomography Computer Science Coronaviruses COVID-19 Datasets Deep learning Health Informatics Lungs Machine learning Medical imaging Neural networks Original Original Article Patients Pneumonia Polymerase chain reaction Respiratory system Reverse transcription Sensitivity Signs and symptoms Viral diseases Viruses X-rays |
title | Predicting pattern of coronavirus using X-ray and CT scan images |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T06%3A32%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20pattern%20of%20coronavirus%20using%20X-ray%20and%20CT%20scan%20images&rft.jtitle=Network%20modeling%20and%20analysis%20in%20health%20informatics%20and%20bioinformatics%20(Wien)&rft.au=Khurana%20Batra,%20Payal&rft.date=2022-12-01&rft.volume=11&rft.issue=1&rft.spage=39&rft.pages=39-&rft.artnum=39&rft.issn=2192-6662&rft.eissn=2192-6670&rft_id=info:doi/10.1007/s13721-022-00382-2&rft_dat=%3Cproquest_pubme%3E2919735091%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2919735091&rft_id=info:pmid/&rfr_iscdi=true |