Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.

Autor: Alyasseri ZAA; Center for Artificial Intelligence Technology, Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia.; ECE Department-Faculty of Engineering University of Kufa Najaf Iraq., Al-Betar MA; Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates.; Department of Information Technology Al-Huson University College, Al-Balqa Applied University Irbid Jordan., Doush IA; Computing Department, College of Engineering and Applied Sciences American University of Kuwait Salmiya Kuwait.; Computer Science Department Yarmouk University Irbid Jordan., Awadallah MA; Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates.; Department of Computer Science Al-Aqsa University Gaza Palestine., Abasi AK; Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates.; School of Computer Sciences Universiti Sains Malaysia Penang Malaysia., Makhadmeh SN; Faculty of Information Technology Middle East University Amman Jordan.; Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates., Alomari OA; MLALP Research Group University of Sharjah Sharjah United Arab Emirates., Abdulkareem KH; College of Agriculture Al-Muthanna University Samawah Iraq., Adam A; Center for Artificial Intelligence Technology, Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia., Damasevicius R; Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland., Mohammed MA; College of Computer Science and Information Technology University of Anbar Anbar Iraq., Zitar RA; Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates.
Jazyk: angličtina
Zdroj: Expert systems [Expert Syst] 2022 Mar; Vol. 39 (3), pp. e12759. Date of Electronic Publication: 2021 Jul 28.
DOI: 10.1111/exsy.12759
Abstrakt: COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
(© 2021 John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje