Guest Editorial: Advanced Deep Learning Techniques for COVID-19


The recent diagnosis of COVID-19 is based on real-Time reverse-Transcriptase polymerase chain reaction (RT-PCR) and is regarded as the gold standard for confirmation of infection. It has already been widely recognized that deep learning techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients. Numerous open dataset enterprises have been set up over the past weeks to help the researchers develop and check methods that could contribute to countering the Corona pandemic. In order to report the above unique problems in the diagnosis of COVID-19, pioneering techniques should be developed. This special issue focuses on novel deep learning imaging analysis techniques related to COVID-19.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Control and Systems Engineering,Information Systems,Computer Science Applications,Electrical and Electronic Engineering
Publication ISSN: 1551-3203
Last Modified: 22 Jul 2024 07:28
Date Deposited: 09 Jun 2022 10:39
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... ocument/9382835 (Publisher URL)
PURE Output Type: Editorial
Published Date: 2021-09-01
Published Online Date: 2021-03-22
Accepted Date: 2021-03-01
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Abdel-Basset, Mohamed
Iqbal, Rahat
Wills, Gary



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record