Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network
Purpose The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach We used machine learning techniques with convolutional neural network. Findings Detecting COVID-19 symptoms from patient CT scan images. Orig...
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| Veröffentlicht in: | World journal of engineering 2022-02, Vol.19 (1), p.90-97 |
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| Hauptverfasser: | , , , , |
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| container_end_page | 97 |
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| container_issue | 1 |
| container_start_page | 90 |
| container_title | World journal of engineering |
| container_volume | 19 |
| creator | Kumar, Pankaj Bajpai, Bhavna Gupta, Deepak Omprakash Jain, Dinesh C. Vimal, S. |
| description | Purpose
The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.
Design/methodology/approach
We used machine learning techniques with convolutional neural network.
Findings
Detecting COVID-19 symptoms from patient CT scan images.
Originality/value
This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images. |
| doi_str_mv | 10.1108/WJE-12-2020-0655 |
| format | Article |
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The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.
Design/methodology/approach
We used machine learning techniques with convolutional neural network.
Findings
Detecting COVID-19 symptoms from patient CT scan images.
Originality/value
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The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.
Design/methodology/approach
We used machine learning techniques with convolutional neural network.
Findings
Detecting COVID-19 symptoms from patient CT scan images.
Originality/value
This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.</description><subject>Artificial neural networks</subject><subject>Computed tomography</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Medical research</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>X-rays</subject><issn>1708-5284</issn><issn>1708-5284</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkL1PwzAQxSMEElXpzmiJ2fTsxE4yorZAUUUXPkbLcS4l_YiLnRTx3-NQBpC44e6G957uflF0yeCaMcjGrw8zyjjlwIGCFOIkGrAUMip4lpz-2s-jkfdrCJVIztJ4EJn5Tq-QODR21dRtbRtiKzJZvsynlOWk83WzIlPtNhN7qMtHbIl25q1u0bSdQ1JojyUJJmObg912fYDekgY79z3aD-s2F9FZpbceRz9zGD3fzp4m93SxvJtPbhbUxCJuaRGjYWkOeaIThikm3EhdYZKKXIgMktABZJ6iLIMGdFmABKwKFsdpaTIeD6OrY-7e2fcOfavWtnPhHq94ymQmhWQQVHBUGWe9d1ipvat32n0qBqqnqQJNxbjqaaqeZrCMjxbcYfir_M_xh3_8BaiEdK4</recordid><startdate>20220222</startdate><enddate>20220222</enddate><creator>Kumar, Pankaj</creator><creator>Bajpai, Bhavna</creator><creator>Gupta, Deepak Omprakash</creator><creator>Jain, Dinesh C.</creator><creator>Vimal, S.</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220222</creationdate><title>Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network</title><author>Kumar, Pankaj ; Bajpai, Bhavna ; Gupta, Deepak Omprakash ; Jain, Dinesh C. ; Vimal, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-b3ec179094a41e7e42c6afe47595580495500697e6d0940adb060efb1337dc823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural networks</topic><topic>Computed tomography</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Medical research</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>X-rays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Pankaj</creatorcontrib><creatorcontrib>Bajpai, Bhavna</creatorcontrib><creatorcontrib>Gupta, Deepak Omprakash</creatorcontrib><creatorcontrib>Jain, Dinesh C.</creatorcontrib><creatorcontrib>Vimal, S.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</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 Central China</collection><collection>Engineering Collection</collection><jtitle>World journal of engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kumar, Pankaj</au><au>Bajpai, Bhavna</au><au>Gupta, Deepak Omprakash</au><au>Jain, Dinesh C.</au><au>Vimal, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network</atitle><jtitle>World journal of engineering</jtitle><date>2022-02-22</date><risdate>2022</risdate><volume>19</volume><issue>1</issue><spage>90</spage><epage>97</epage><pages>90-97</pages><issn>1708-5284</issn><eissn>1708-5284</eissn><abstract>Purpose
The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.
Design/methodology/approach
We used machine learning techniques with convolutional neural network.
Findings
Detecting COVID-19 symptoms from patient CT scan images.
Originality/value
This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.</abstract><cop>Brentwood</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/WJE-12-2020-0655</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1708-5284 |
| ispartof | World journal of engineering, 2022-02, Vol.19 (1), p.90-97 |
| issn | 1708-5284 1708-5284 |
| language | eng |
| recordid | cdi_proquest_journals_2716865610 |
| source | Standard: Emerald eJournal Premier Collection |
| subjects | Artificial neural networks Computed tomography Coronaviruses COVID-19 Datasets Machine learning Medical imaging Medical research Neural networks Object recognition X-rays |
| title | Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network |
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