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Medical image processing and COVID-19: A literature review and bibliometric analysis.
Abumalloh, Rabab Ali; Nilashi, Mehrbakhsh; Yousoof Ismail, Muhammed; Alhargan, Ashwaq; Alghamdi, Abdullah; Alzahrani, Ahmed Omar; Saraireh, Linah; Osman, Reem; Asadi, Shahla.
  • Abumalloh RA; Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia.
  • Nilashi M; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800, USM Penang, Malaysia. Electronic address: nilashidotnet@hotmail.com.
  • Yousoof Ismail M; Department of MIS, Dhofar University, Oman.
  • Alhargan A; Computer Science Department, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia.
  • Alghamdi A; Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
  • Alzahrani AO; College of Computer Science and Engineering, University of Jeddah, 21959 Jeddah, Saudi Arabia.
  • Saraireh L; Management Information System Department, College of Business, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia.
  • Osman R; Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia.
  • Asadi S; Centre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
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.
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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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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