An outbreak of a unique coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China considering the fact that overdue December 2019, which ultimately has become a deadly disease across the world. Although COVID-19 is an acutely dealt with disease, it may additionally be deadly with a hazard of fatality of 4.03% in China and the best of 13.04Algeria and 12.67% in Italy (as of eighth April 2020). The onset of significant contamination may also bring about dying on account of vast alveolar harm and innovative breathing failure. Although laboratory trying out, e.g., the usage of reverse transcription polymerase chain reaction (RT-PCR), is the golden widespread for scientific prognosis, the checks may also produce fake negatives. Moreover, below the pandemic situation, a scarcity of RTPCR trying outsources may additionally put off the subsequent scientific choice and treatment. Under such circumstances, there are many methods for prognosis and analysis of COVID-19 patients. In this survey paper, various deep learning techniques for detection of COVID-19 are discussed.

1.
M. M. Rachna
Sethi
and
D.
Sethi
, "
Deep learning-based diagnosis recommendation for covid-19 using chest x-rays images
," (
2020
).
2.
H. S. Z. A. B. Abolfazl Karimiyan
Abdar
,
Seyyed Mostafa
Sadjadi
and
M.
Naghibi
, (
2020
).
3.
A. K. R. M. M.A. K. Z. B. M. K. R. I. M. S. K. A. I. N. A. E. M. B. I. R
.
Muhammad
E. H.
Chowdhury
,
Tawsifur
Rahman
and
M. T.
Islam
,"
Can ai help in screening viral and covid-19 pneumonia?
"
IEEE Access
8
(
2020
).
4.
P. O. A. L. S. F. L. R. F
.
Sivaramakrishnan Rajaraman
,
Jenifer
Siegelman
and
S. K.
Antani
, "
Iteratively pruned deep learning ensembles forcovid-19 detection in chest x-rays
,"
IEEE Access
8
(
2020
).
5.
J. C. S. C. H. Z. Y. Z. J. W. L. L. W. S. T. Q. K. M. H. X
.
Yuexiang Li
,
Dong
Wei
and
Y.
Zheng
, "
Efficient and effective training of covid-19 classification networks with self-supervised dual-track learning to rank
,"
IEEE Journal of Biomedical and Health Informatics
24
(
2020
).
6.
Y. J. L. L. X. X. M. W. E. F. F. W. M.-S. J. X. H. Y.
Shaoping
Hu
,
Yuan Gao
;
Zhangming
Niu
and
G.
Yang
, "
Weakly supervised deep learningfor covid-19 infection detection and classification from ct images
," (
2020
).
7.
T.
Anwar
and
S.
Zakir
, "
Deep learning-based diagnosis of covid-19 using chest ct-scan images
,"
2020 IEEE 23rd International MultitopicConference (INMIC)
(
2020
).
8.
G.-P. J. Y. Z. G. C.H. F. J. S. L. S
.
Deng-Ping Fan
,
Tao
Zhou
,
"Inf-net: Automatic covid-19 lung infection segmentation from ct irnages
, "
IEEE Transactions on Medical Imaging
39
(
2020
).
9.
M. Z. M. U. R. N. H. W. M. P
.
Ahmed Mohammed
,
Congcong
Wang
and
F. A.
Cheikh
, "
Weakly-supervised network for detection ofcovid-19 in chest ct scans
,"
IEEE Access
8
(
2020
).
10.
L. Z. G. L. Z. M
.
Tao Wang
,
Yongguo
Zhao
and
J.
Zheng
, "
Lung ct image aided detection covid-19 based on alexnet network
,"
2020 5thlnternational Conference on Communication, Image and Signal Processing (CCISP)
(
2020
).
11.
L. z. W. Y. z
.
Jingxin Liu
,
Zhong
Zhang
, "
Intelligent detection for ct image of covid-19 using deep learning
,"
2020 13th International Congresson Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
(
2020
).
12.
Q. F. Z. J. F. M. W. L
.
Xinggang Wang
,
Xianbo
Deng
and
C.
Zheng
, "
A weakly-supervised framework for covid-19 classification and lesionlocalization from chest ct
,"
IEEE Transactions on Medical Imaging
39
(
2020
).
This content is only available via PDF.
You do not currently have access to this content.