A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach

Abstract

The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost
every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1,
2020. It has been spreading ever since, and the peak specific to every country has been rising and falling and does
not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the
alternative method for data archiving purposes is certainly another choice for public departments to make.
Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose
the virus with the aid of Artificial Intelligence (AI)-based software. This review paper provides an investigation
of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other
methods of tests performed by medical experts. The facilities of computer vision enable us to develop an
automated model that has clinical abilities of early detection of the disease. We have explored the researchers’
focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to
test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying
COVID-19 using machine learning and future work studies. This review's findings can be useful for public and
private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks,
enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the
pandemic.