mlsanityData to 14 Oct 2026

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

arXiv 2108.04344 ↗First posted 25 Jul 2021cs.CV cs.LG eess.IVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 64 of its 126 references that are in the map. First posted on arXiv 25 Jul 2021.

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64 of 126 references.

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38 more
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