  Due to the limited availability and high cost of the reverse
transcription-polymerase chain reaction (RT-PCR) test, many studies have
proposed machine learning techniques for detecting COVID-19 from medical
imaging. The purpose of this study is to systematically review, assess, and
synthesize research articles that have used different machine learning
techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.
A structured literature search was conducted in the relevant bibliographic
databases to ensure that the survey solely centered on reproducible and
high-quality research. We selected papers based on our inclusion criteria. In
this survey, we reviewed $98$ articles that fulfilled our inclusion criteria.
We have surveyed a complete pipeline of chest imaging analysis techniques
related to COVID-19, including data collection, pre-processing, feature
extraction, classification, and visualization. We have considered CT scans and
X-rays as both are widely used to describe the latest developments in medical
imaging to detect COVID-19. This survey provides researchers with valuable
insights into different machine learning techniques and their performance in
the detection and diagnosis of COVID-19 from chest imaging. At the end, the
challenges and limitations in detecting COVID-19 using machine learning
techniques and the future direction of research are discussed.
