Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective

Authors

  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Iraq https://orcid.org/0000-0002-2884-2504
  • Karan Aggarwal Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to be University), India
  • Ruchi Doshi Department of Computer Science and Engineering, Universidad Azteca, Mexico
  • Kamal Kant Hiran Department of Electronics Systems, Aalborg University Copenhagen, Denmark
  • B. Sundaravadivazhagan University of Technology and Applied sciences, Oman

DOI:

https://doi.org/10.24203/ajas.v10i3.6998

Keywords:

COVID-19, Deep Learning, Machine Learning, Artificial Intelligence, Chest X-ray, CT-scan

Abstract

Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical domain, which relies heavily on artificial intelligence techniques in diagnosing disease data and manufacturing drugs and vaccines. Artificial intelligence has unexpectedly advanced in helping physicians and healthcare workers save many lives, especially during the spread of the COVID-19 virus. This article reviews some literature that have applied deep learning techniques to detect COVID-19 based on chest x-rays and CT-scans images. This article concluded that deep learning techniques have a fundamental and significant role in diagnosing a big dataset of images and assisting specialists in determining whether a person is infected (positive cases).

 

Author Biography

Maad M. Mijwil, Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Iraq

Maad M. Mijwil received B.Sc. degree in Software Engineering from Software Engineering Department at Baghdad College of Economics Sciences University, Iraq in 2008/2009 and M.Sc. degree in Wireless sensor network of computer science from University of Baghdad, Iraq in 2015. Currently he is working Assistant Lecturer at Baghdad College of Economics Sciences University.

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2022-07-09

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Mijwil, M. M., Aggarwal, K. ., Doshi, R., Hiran, K. K. . ., & Sundaravadivazhagan, B. (2022). Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective. Asian Journal of Applied Sciences, 10(3). https://doi.org/10.24203/ajas.v10i3.6998

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