Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review-Reference-Cited by-同舟云学术

Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

Published:2022 Issue: Volume:10 Page:98633-98648
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sadak Omer¹,Sadak Ferhat²,Yildirim Ozal³,Iverson Nicole M.⁴,Qureshi Rizwan⁵,Talo Muhammed⁶,Ooi Chui Ping⁷,Acharya U. Rajendra⁷,Gunasekaran Sundaram⁸,Alam Tanvir⁵^ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, Ardahan University, Ardahan, Turkey

2. Department of Mechanical Engineering, Bartin University, Bartin, Turkey

3. Department of Software Engineering, Firat University, Elaziğ, Turkey

4. Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA

5. College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

6. Department of Industrial Engineering, Firat University, Elaziğ, Turkey

7. School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Rd, Singapore

8. Department of Biological Systems Engineering, University of Wisconsin–Madison, Madison, WI, USA

Funder

College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha, Qatar. Open Access publication of this article was funded by Qatar National Library (QNL), Doha, Qatar

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09893825.pdf?arnumber=9893825

Reference125 articles.

1. Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

2. The Role of Imaging in the Detection and Management of COVID-19: A Review

3. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

4. Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

5. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

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