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An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.

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Title: An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.
Authors: Pustokhin, Denis A.1, Pustokhina, Irina V.2, Dinh, Phuoc Nguyen3, Phan, Son Van4,5, Nguyen, Gia Nhu4,5, nguyengianhu@duytan.edu.vn, Joshi, Gyanendra Prasad6, K., Shankar7
Source: Journal of Applied Statistics; Mar2023, Vol. 50 Issue 3, p477-494, 18p, 1 Black and White Photograph, 5 Diagrams, 2 Charts, 4 Graphs
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 161832216
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=161832216
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/02664763.2020.1849057
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 477
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      – TitleFull: An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.
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            NameFull: Pustokhin, Denis A.
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            NameFull: Pustokhina, Irina V.
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            NameFull: Dinh, Phuoc Nguyen
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            NameFull: Phan, Son Van
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            NameFull: Nguyen, Gia Nhu
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            NameFull: Joshi, Gyanendra Prasad
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            – D: 01
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
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            – TitleFull: Journal of Applied Statistics
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