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

Aishwarza Panday, Ashad Kabir, Nihad K. Chowdhury

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalQuantitative Biology
Publication statusE-pub ahead of print - 18 Mar 2022


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