A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images-Reference-Cited by-同舟云学术

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A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images

Published:2024-02 Issue:1 Volume:70 Page:2794-2805
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Liu Yiwen¹^ORCID,Xing Wenyu²^ORCID,Lin Mingquan³,Liu Yuping⁴^ORCID,Chow Tommy W. S.⁵^ORCID

Affiliation:

1. School of Information Science and Technology, Donghua University, Shanghai, China

2. Academy for Engineering and Technology, Fudan University, Shanghai, China

3. Department of Surgery, University of Minnesota, Minneapolis, MN, USA

4. School of Microelectronics, Fudan University, Shanghai, China

5. City University of Hong Kong, Hong Kong, Hong Kong, S.A.R, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10440137.pdf?arnumber=10440137

Reference49 articles.

1. Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

2. Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

3. A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID

4. Detection of COVID-19: A review of the current literature and future perspectives

5. Review of analytical performance of COVID-19 detection methods

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