Cargando…

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are...

Descripción completa

Detalles Bibliográficos
Autores principales: Alsaaidah, Bayan, Al-Hadidi, Moh’d Rasoul, Al-Nsour, Heba, Masadeh, Raja, AlZubi, Nael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604704/
https://www.ncbi.nlm.nih.gov/pubmed/36286361
http://dx.doi.org/10.3390/jimaging8100267
_version_ 1784817881373999104
author Alsaaidah, Bayan
Al-Hadidi, Moh’d Rasoul
Al-Nsour, Heba
Masadeh, Raja
AlZubi, Nael
author_facet Alsaaidah, Bayan
Al-Hadidi, Moh’d Rasoul
Al-Nsour, Heba
Masadeh, Raja
AlZubi, Nael
author_sort Alsaaidah, Bayan
collection PubMed
description The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
format Online
Article
Text
id pubmed-9604704
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96047042022-10-27 Comprehensive Survey of Machine Learning Systems for COVID-19 Detection Alsaaidah, Bayan Al-Hadidi, Moh’d Rasoul Al-Nsour, Heba Masadeh, Raja AlZubi, Nael J Imaging Review The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification. MDPI 2022-09-30 /pmc/articles/PMC9604704/ /pubmed/36286361 http://dx.doi.org/10.3390/jimaging8100267 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Alsaaidah, Bayan
Al-Hadidi, Moh’d Rasoul
Al-Nsour, Heba
Masadeh, Raja
AlZubi, Nael
Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title_full Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title_fullStr Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title_full_unstemmed Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title_short Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
title_sort comprehensive survey of machine learning systems for covid-19 detection
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604704/
https://www.ncbi.nlm.nih.gov/pubmed/36286361
http://dx.doi.org/10.3390/jimaging8100267
work_keys_str_mv AT alsaaidahbayan comprehensivesurveyofmachinelearningsystemsforcovid19detection
AT alhadidimohdrasoul comprehensivesurveyofmachinelearningsystemsforcovid19detection
AT alnsourheba comprehensivesurveyofmachinelearningsystemsforcovid19detection
AT masadehraja comprehensivesurveyofmachinelearningsystemsforcovid19detection
AT alzubinael comprehensivesurveyofmachinelearningsystemsforcovid19detection