A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud

There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease ear...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:NeuroQuantology 2022-01, Vol.20 (11), p.4252
Hauptverfasser: Al-Shaibani, Safwan A S, Bhalchandra, Parag, Kapse, Vijaykumar
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 11
container_start_page 4252
container_title NeuroQuantology
container_volume 20
creator Al-Shaibani, Safwan A S
Bhalchandra, Parag
Kapse, Vijaykumar
description There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease early by using chest X-rays to classify whether or not the disease is present. Note that doctors have agreed in more than one scientific article that the initial examination to detect this disease is carried out through chest x-rays, the devices of which are available in most places.Because the Internet is available in most rural areas and in order to reduce the spread of this pandemic, in this paper we built a project by deep transfer learning using an application in Keras called "InceptionV3" on cloud, this model trained and tested 10 thousand images of people with the disease and others where the data distribution was equal to avoid From imbalanced data, and this model will be used across the cloud by web framework so that we can get proactive decisions and avoid spread. This model has been applied in the Department of Respiratory Medicine at Dr. ShankarraoChavan Government Hospital, Nanded, under the supervision of a medical staff headed by Dr. V. R. Kapse, associate professor and head of the department of pulmonary, we have obtained results after training and evaluating the model are training accuracy 97.6%, testing accuracy 97.5%, precision 97.8%, sensitivity 100% and specificity 99.9%.
doi_str_mv 10.14704/nq.2022.20.11.NQ66430
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2901738802</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2901738802</sourcerecordid><originalsourceid>FETCH-proquest_journals_29017388023</originalsourceid><addsrcrecordid>eNqNjN1qwkAUhBehoNW-ghzoddKzu_m9lKhUKC1CKb0Li25Mou7R3WyLb2-kPkBvZhi-mWFsyjHkUYrRizmHAoXoJeQ8fF8nSSRxwEZcogxiHuOQPTrXIsYp5smI6RksrTrqX7J7mPmOjqpryABVUHx8reYBz6E4KOeaqtn8oa625Hc1FLV2HXwHVl0ceNeYHcy1PsGbVtbcEv1o24_JbyfsoVIHp5_uPmbPy8Vn8RqcLJ19f1O25K3pUSly5KnMMhTyf60rG0JLPA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2901738802</pqid></control><display><type>article</type><title>A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Al-Shaibani, Safwan A S ; Bhalchandra, Parag ; Kapse, Vijaykumar</creator><creatorcontrib>Al-Shaibani, Safwan A S ; Bhalchandra, Parag ; Kapse, Vijaykumar</creatorcontrib><description>There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease early by using chest X-rays to classify whether or not the disease is present. Note that doctors have agreed in more than one scientific article that the initial examination to detect this disease is carried out through chest x-rays, the devices of which are available in most places.Because the Internet is available in most rural areas and in order to reduce the spread of this pandemic, in this paper we built a project by deep transfer learning using an application in Keras called "InceptionV3" on cloud, this model trained and tested 10 thousand images of people with the disease and others where the data distribution was equal to avoid From imbalanced data, and this model will be used across the cloud by web framework so that we can get proactive decisions and avoid spread. This model has been applied in the Department of Respiratory Medicine at Dr. ShankarraoChavan Government Hospital, Nanded, under the supervision of a medical staff headed by Dr. V. R. Kapse, associate professor and head of the department of pulmonary, we have obtained results after training and evaluating the model are training accuracy 97.6%, testing accuracy 97.5%, precision 97.8%, sensitivity 100% and specificity 99.9%.</description><identifier>EISSN: 1303-5150</identifier><identifier>DOI: 10.14704/nq.2022.20.11.NQ66430</identifier><language>eng</language><publisher>Bornova Izmir: NeuroQuantology</publisher><subject>Artificial intelligence ; COVID-19 ; Deep learning ; Machine learning ; Medical personnel ; Model accuracy ; Model testing ; Pandemics ; Rural areas ; X-rays</subject><ispartof>NeuroQuantology, 2022-01, Vol.20 (11), p.4252</ispartof><rights>Copyright NeuroQuantology 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Al-Shaibani, Safwan A S</creatorcontrib><creatorcontrib>Bhalchandra, Parag</creatorcontrib><creatorcontrib>Kapse, Vijaykumar</creatorcontrib><title>A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud</title><title>NeuroQuantology</title><description>There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease early by using chest X-rays to classify whether or not the disease is present. Note that doctors have agreed in more than one scientific article that the initial examination to detect this disease is carried out through chest x-rays, the devices of which are available in most places.Because the Internet is available in most rural areas and in order to reduce the spread of this pandemic, in this paper we built a project by deep transfer learning using an application in Keras called "InceptionV3" on cloud, this model trained and tested 10 thousand images of people with the disease and others where the data distribution was equal to avoid From imbalanced data, and this model will be used across the cloud by web framework so that we can get proactive decisions and avoid spread. This model has been applied in the Department of Respiratory Medicine at Dr. ShankarraoChavan Government Hospital, Nanded, under the supervision of a medical staff headed by Dr. V. R. Kapse, associate professor and head of the department of pulmonary, we have obtained results after training and evaluating the model are training accuracy 97.6%, testing accuracy 97.5%, precision 97.8%, sensitivity 100% and specificity 99.9%.</description><subject>Artificial intelligence</subject><subject>COVID-19</subject><subject>Deep learning</subject><subject>Machine learning</subject><subject>Medical personnel</subject><subject>Model accuracy</subject><subject>Model testing</subject><subject>Pandemics</subject><subject>Rural areas</subject><subject>X-rays</subject><issn>1303-5150</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNjN1qwkAUhBehoNW-ghzoddKzu_m9lKhUKC1CKb0Li25Mou7R3WyLb2-kPkBvZhi-mWFsyjHkUYrRizmHAoXoJeQ8fF8nSSRxwEZcogxiHuOQPTrXIsYp5smI6RksrTrqX7J7mPmOjqpryABVUHx8reYBz6E4KOeaqtn8oa625Hc1FLV2HXwHVl0ceNeYHcy1PsGbVtbcEv1o24_JbyfsoVIHp5_uPmbPy8Vn8RqcLJ19f1O25K3pUSly5KnMMhTyf60rG0JLPA</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Al-Shaibani, Safwan A S</creator><creator>Bhalchandra, Parag</creator><creator>Kapse, Vijaykumar</creator><general>NeuroQuantology</general><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88G</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2M</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope></search><sort><creationdate>20220101</creationdate><title>A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud</title><author>Al-Shaibani, Safwan A S ; Bhalchandra, Parag ; Kapse, Vijaykumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_29017388023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>COVID-19</topic><topic>Deep learning</topic><topic>Machine learning</topic><topic>Medical personnel</topic><topic>Model accuracy</topic><topic>Model testing</topic><topic>Pandemics</topic><topic>Rural areas</topic><topic>X-rays</topic><toplevel>online_resources</toplevel><creatorcontrib>Al-Shaibani, Safwan A S</creatorcontrib><creatorcontrib>Bhalchandra, Parag</creatorcontrib><creatorcontrib>Kapse, Vijaykumar</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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 &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology 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 Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Psychology Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; 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>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><jtitle>NeuroQuantology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Al-Shaibani, Safwan A S</au><au>Bhalchandra, Parag</au><au>Kapse, Vijaykumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud</atitle><jtitle>NeuroQuantology</jtitle><date>2022-01-01</date><risdate>2022</risdate><volume>20</volume><issue>11</issue><spage>4252</spage><pages>4252-</pages><eissn>1303-5150</eissn><abstract>There is a huge the spread of Covid-19 pandemic (Corona) in large areas of the country, including modern and rural areas, and due to the scarcity of medical tools and supplies, especially in rural areas. Therefore, artificial intelligence researchers are using technologies to help detect disease early by using chest X-rays to classify whether or not the disease is present. Note that doctors have agreed in more than one scientific article that the initial examination to detect this disease is carried out through chest x-rays, the devices of which are available in most places.Because the Internet is available in most rural areas and in order to reduce the spread of this pandemic, in this paper we built a project by deep transfer learning using an application in Keras called "InceptionV3" on cloud, this model trained and tested 10 thousand images of people with the disease and others where the data distribution was equal to avoid From imbalanced data, and this model will be used across the cloud by web framework so that we can get proactive decisions and avoid spread. This model has been applied in the Department of Respiratory Medicine at Dr. ShankarraoChavan Government Hospital, Nanded, under the supervision of a medical staff headed by Dr. V. R. Kapse, associate professor and head of the department of pulmonary, we have obtained results after training and evaluating the model are training accuracy 97.6%, testing accuracy 97.5%, precision 97.8%, sensitivity 100% and specificity 99.9%.</abstract><cop>Bornova Izmir</cop><pub>NeuroQuantology</pub><doi>10.14704/nq.2022.20.11.NQ66430</doi></addata></record>
fulltext fulltext
identifier EISSN: 1303-5150
ispartof NeuroQuantology, 2022-01, Vol.20 (11), p.4252
issn 1303-5150
language eng
recordid cdi_proquest_journals_2901738802
source EZB-FREE-00999 freely available EZB journals
subjects Artificial intelligence
COVID-19
Deep learning
Machine learning
Medical personnel
Model accuracy
Model testing
Pandemics
Rural areas
X-rays
title A Framework Automation of COVID-19 Classification through Chest X-rays using Deep Learning over Cloud
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T11%3A37%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Framework%20Automation%20of%20COVID-19%20Classification%20through%20Chest%20X-rays%20using%20Deep%20Learning%20over%20Cloud&rft.jtitle=NeuroQuantology&rft.au=Al-Shaibani,%20Safwan%20A%20S&rft.date=2022-01-01&rft.volume=20&rft.issue=11&rft.spage=4252&rft.pages=4252-&rft.eissn=1303-5150&rft_id=info:doi/10.14704/nq.2022.20.11.NQ66430&rft_dat=%3Cproquest%3E2901738802%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2901738802&rft_id=info:pmid/&rfr_iscdi=true