Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique.
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| Title: | Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique. |
|---|---|
| Authors: | Vidhya, K.1 vidhya.k@kpriet.ac.in, B., Nagarajan2, Kalaiselvi, R.3 |
| Source: | Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 3, p1-9. 9p. |
| Subject Terms: | COVID-19, X-ray imaging, Computed tomography, Data augmentation, COVID-19 testing, Deep learning |
| Abstract: | Covid 19 is the life threatening disease which spreads widely and rapidly. It mainly affects the breathing organs of our body as a first feat and then it causes various related infections gradually. Right from the day to day activity of human to economic growth of the whole world are totally affected by this deadly virus. Covid-19 could be a communal transmissible disease, and one nation could prepare a COVID-19 antibody alert. Clinical assumptions about COVID-19 diseased patients have exposed that these types of patients are largely adulterated with lung disease after exposure to the disease. So early prediction of COVID 19 related infection is the need for the day. Chest x-ray (i.e., radiography) and chest CT is the utmost operative imaging modalities for spotting complications. Nevertheless, a big chest x-ray may be a lesser significance likened to a CT scan. In-depth interpretation is the most prolific process of machine learning, given that valued research to consider the large number of chest x-ray images that can have a substantial influence on Covid-19 testing. The proposed work examines the PA form of chest X-ray images of Covid 19 affected patients and healthy patients. Once after collection of data through augmentation, the classification is performed by ResNet 50 model with the Deep Belief Network (DBN). By the way the system identifies the patients affected by COVID 19.In this analysis, chest X-ray samples were collected from the COVID -Xray-5k database. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 160413428 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vidhya%2C+K%2E%22">Vidhya, K.</searchLink><relatesTo>1</relatesTo><i> vidhya.k@kpriet.ac.in</i><br /><searchLink fieldCode="AR" term="%22B%2E%2C+Nagarajan%22">B., Nagarajan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kalaiselvi%2C+R%2E%22">Kalaiselvi, R.</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Qualitative+Inquiry%22">Turkish Online Journal of Qualitative Inquiry</searchLink>. 2021, Vol. 12 Issue 3, p1-9. 9p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+testing%22">COVID-19 testing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Covid 19 is the life threatening disease which spreads widely and rapidly. It mainly affects the breathing organs of our body as a first feat and then it causes various related infections gradually. Right from the day to day activity of human to economic growth of the whole world are totally affected by this deadly virus. Covid-19 could be a communal transmissible disease, and one nation could prepare a COVID-19 antibody alert. Clinical assumptions about COVID-19 diseased patients have exposed that these types of patients are largely adulterated with lung disease after exposure to the disease. So early prediction of COVID 19 related infection is the need for the day. Chest x-ray (i.e., radiography) and chest CT is the utmost operative imaging modalities for spotting complications. Nevertheless, a big chest x-ray may be a lesser significance likened to a CT scan. In-depth interpretation is the most prolific process of machine learning, given that valued research to consider the large number of chest x-ray images that can have a substantial influence on Covid-19 testing. The proposed work examines the PA form of chest X-ray images of Covid 19 affected patients and healthy patients. Once after collection of data through augmentation, the classification is performed by ResNet 50 model with the Deep Belief Network (DBN). By the way the system identifies the patients affected by COVID 19.In this analysis, chest X-ray samples were collected from the COVID -Xray-5k database. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: COVID-19 Type: general – SubjectFull: X-ray imaging Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: COVID-19 testing Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vidhya, K. – PersonEntity: Name: NameFull: B., Nagarajan – PersonEntity: Name: NameFull: Kalaiselvi, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13096591 Numbering: – Type: volume Value: 12 – Type: issue Value: 3 Titles: – TitleFull: Turkish Online Journal of Qualitative Inquiry Type: main |
| ResultId | 1 |
