Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.
Journal
Journal of healthcare engineering
ISSN: 2040-2309
Titre abrégé: J Healthc Eng
Pays: England
ID NLM: 101528166
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
12
2020
revised:
08
01
2021
accepted:
11
02
2021
entrez:
22
3
2021
pubmed:
23
3
2021
medline:
7
4
2021
Statut:
epublish
Résumé
The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.
Identifiants
pubmed: 33747419
doi: 10.1155/2021/6677314
pmc: PMC7958142
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
6677314Commentaires et corrections
Type : ErratumIn
Informations de copyright
Copyright © 2021 Mustafa Ghaderzadeh and Farkhondeh Asadi.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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