On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review

Mohammed Abdulazeem Siddiqui; Mohammed Akber Ali; Mohamed Deriche

doi:10.1109/SSD52085.2021.9429345

On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review

Mohammed Abdulazeem Siddiqui
, Mohammed Akber Ali
, Mohamed Deriche

King Fahd University of Petroleum and Minerals

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

The outbreak of the new coronavirus (COVID19), has caused devastating effects and was declared as a major pandemic by the World Health Organization (WHO). Apart from knowing the main causes, it's very important to timely diagnose the virus in an individual, so that treatment and isolation (if needed) can start as early as possible and spread of the virus is contained among the healthy people. In this research, we discuss various machine learning (ML) and deep learning (DL) approaches that have been proposed for the diagnosis of the virus using different bio-indicators with particular focus on lungs imaging. A detailed analysis of existing techniques is presented with future perspective on the use of new machine learning techniques for the diagnosis of the COVID19 and other similar viruses.

Original language	English
Title of host publication	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665414937
DOIs	https://doi.org/10.1109/SSD52085.2021.9429345
State	Published - 22 Mar 2021
Externally published	Yes
Event	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 - Monastir, Tunisia Duration: 22 Mar 2021 → 25 Mar 2021

Publication series

Name	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

Conference

Conference	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
Country/Territory	Tunisia
City	Monastir
Period	22/03/21 → 25/03/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Automatic Detection
COVID19
CT Scan
Deep learning
ML
Ultrasound
Xray

Access to Document

10.1109/SSD52085.2021.9429345

Cite this

Siddiqui, M. A., Ali, M. A., & Deriche, M. (2021). On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review. In 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 Article 9429345 (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD52085.2021.9429345

Siddiqui, Mohammed Abdulazeem ; Ali, Mohammed Akber ; Deriche, Mohamed. / On the Early Detection of COVID19 using Advanced Machine Learning Techniques : A Review. 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021).

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title = "On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review",

abstract = "The outbreak of the new coronavirus (COVID19), has caused devastating effects and was declared as a major pandemic by the World Health Organization (WHO). Apart from knowing the main causes, it's very important to timely diagnose the virus in an individual, so that treatment and isolation (if needed) can start as early as possible and spread of the virus is contained among the healthy people. In this research, we discuss various machine learning (ML) and deep learning (DL) approaches that have been proposed for the diagnosis of the virus using different bio-indicators with particular focus on lungs imaging. A detailed analysis of existing techniques is presented with future perspective on the use of new machine learning techniques for the diagnosis of the COVID19 and other similar viruses.",

keywords = "Automatic Detection, COVID19, CT Scan, Deep learning, ML, Ultrasound, Xray",

author = "Siddiqui, \{Mohammed Abdulazeem\} and Ali, \{Mohammed Akber\} and Mohamed Deriche",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 ; Conference date: 22-03-2021 Through 25-03-2021",

year = "2021",

month = mar,

day = "22",

doi = "10.1109/SSD52085.2021.9429345",

language = "English",

series = "18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Siddiqui, MA, Ali, MA & Deriche, M 2021, On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review. in 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021., 9429345, 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Institute of Electrical and Electronics Engineers Inc., 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Monastir, Tunisia, 22/03/21. https://doi.org/10.1109/SSD52085.2021.9429345

On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review. / Siddiqui, Mohammed Abdulazeem; Ali, Mohammed Akber; Deriche, Mohamed.
18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9429345 (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On the Early Detection of COVID19 using Advanced Machine Learning Techniques

T2 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

AU - Siddiqui, Mohammed Abdulazeem

AU - Ali, Mohammed Akber

AU - Deriche, Mohamed

PY - 2021/3/22

Y1 - 2021/3/22

N2 - The outbreak of the new coronavirus (COVID19), has caused devastating effects and was declared as a major pandemic by the World Health Organization (WHO). Apart from knowing the main causes, it's very important to timely diagnose the virus in an individual, so that treatment and isolation (if needed) can start as early as possible and spread of the virus is contained among the healthy people. In this research, we discuss various machine learning (ML) and deep learning (DL) approaches that have been proposed for the diagnosis of the virus using different bio-indicators with particular focus on lungs imaging. A detailed analysis of existing techniques is presented with future perspective on the use of new machine learning techniques for the diagnosis of the COVID19 and other similar viruses.

AB - The outbreak of the new coronavirus (COVID19), has caused devastating effects and was declared as a major pandemic by the World Health Organization (WHO). Apart from knowing the main causes, it's very important to timely diagnose the virus in an individual, so that treatment and isolation (if needed) can start as early as possible and spread of the virus is contained among the healthy people. In this research, we discuss various machine learning (ML) and deep learning (DL) approaches that have been proposed for the diagnosis of the virus using different bio-indicators with particular focus on lungs imaging. A detailed analysis of existing techniques is presented with future perspective on the use of new machine learning techniques for the diagnosis of the COVID19 and other similar viruses.

KW - Automatic Detection

KW - COVID19

KW - CT Scan

KW - Deep learning

KW - ML

KW - Ultrasound

KW - Xray

UR - https://www.scopus.com/pages/publications/85107497320

U2 - 10.1109/SSD52085.2021.9429345

DO - 10.1109/SSD52085.2021.9429345

M3 - Conference contribution

AN - SCOPUS:85107497320

T3 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

BT - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 22 March 2021 through 25 March 2021

ER -

Siddiqui MA, Ali MA, Deriche M. On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review. In 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9429345. (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021). doi: 10.1109/SSD52085.2021.9429345

On the Early Detection of COVID19 using Advanced Machine Learning Techniques: A Review

Abstract

Publication series

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UN SDGs

Keywords

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