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...
Gespeichert in:
Veröffentlicht in: | NeuroQuantology 2022-01, Vol.20 (11), p.4252 |
---|---|
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 | 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 & 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 & 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 & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Psychology 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>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 |