A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging
Quantitative Biology
; 10(2):188-207, 2022.
Article
in English
| Scopus | ID: covidwho-1955124
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. © The Author(s) 2022.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
Quantitative Biology
Year:
2022
Document Type:
Article
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