An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19-Reference-Cited by-同舟云学术

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

Published:2020-11-24 Issue: Volume: Page:1-18
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Pustokhin Denis A.¹,Pustokhina Irina V.²,Dinh Phuoc Nguyen³,Phan Son Van⁴⁵,Nguyen Gia Nhu⁴⁵,Joshi Gyanendra Prasad⁶,K. Shankar⁷

Affiliation:

1. Department of Logistics, State University of Management, Moscow, Russia

2. Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, Russia

3. Information Center, University of Natural Science, Ho Chi Minh city, Viet Nam

4. Faculty of Information Technology, Duy Tan University, Da Nang, Viet Nam

5. Graduate School, Duy Tan University, Da Nang, Viet Nam

6. Department of Computer Science and Engineering, Sejong University, Seoul, South Korea

7. Department of Computer Applications, Alagappa University, Karaikudi, India

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2020.1849057

Reference29 articles.

1. Bounded-depth threshold circuits for computer-assisted CT image classification

2. A secure Internet of Things medical information sharing and emergency notification system based on nonrepudiation mechanism

3. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

4. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

5. MERS-CoV: Middle East respiratory syndrome corona virus: Can radiology be of help? Initial single center experience

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