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Record #61046

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Publisher
Springer
Original paper date
Nov 21, 2020
Institution
Communications Engineering Techniques Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq · Computer Science Department, Al-Ma’aref University College, Anbar, Iraq · College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq · College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq · Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia · Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia · School of Computer Science, Guangzhou University, Guangzhou, China · eVIDA Lab, The University of Deusto, Avda/Universidades 24, 48007, Bilbao, Spain
Open access
No
Article type
Research Article
Broad categories
(B/T) Data Science(HSC) Medicine - Infectious Disease(HSC) Medicine - Pulmonology(HSC) Radiology/Imaging
Citation lifecycle

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Pre-retraction
115
Same day
0
Post-retraction
21
% post-retraction
15.4%
~31.4 citations/yr before retraction · ~9.7 citations/yr after
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