Medical image processing and COVID-19: A literature review and bibliometric analysis.
J Infect Public Health
; 15(1): 75-93, 2022 Jan.
Article
in English
| MEDLINE | ID: covidwho-1517347
ABSTRACT
COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Experimental Studies
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
J Infect Public Health
Journal subject:
Communicable Diseases
/
Public Health
Year:
2022
Document Type:
Article
Affiliation country:
J.jiph.2021.11.013
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