A survey of machine learning techniques for detecting and diagnosing COVID‐19 from imaging

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

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