TY - JOUR
T1 - Guest Editorial: Advanced Deep Learning Techniques for COVID-19
AU - Chang, Victor
AU - Abdel-Basset, Mohamed
AU - Iqbal, Rahat
AU - Wills, Gary
N1 - © 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.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85103300820&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/document/9382835
U2 - 10.1109/TII.2021.3067670
DO - 10.1109/TII.2021.3067670
M3 - Editorial
AN - SCOPUS:85103300820
SN - 1551-3203
VL - 17
SP - 6476
EP - 6479
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 9
M1 - 9382835
ER -