A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging

TL;DR

This survey reviews 98 studies on machine learning methods applied to chest X-ray and CT images for COVID-19 detection, highlighting current techniques, challenges, and future research directions.

Contribution

It systematically synthesizes recent high-quality research on machine learning for COVID-19 diagnosis from medical imaging, covering the entire analysis pipeline.

Findings

Various machine learning techniques show promising accuracy in COVID-19 detection.

Challenges include data quality, model generalization, and interpretability.

Future research should focus on robust, explainable models and larger datasets.

Abstract

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.