{
  "id": "2108.04344",
  "version": "v1",
  "published": "2021-07-25T12:26:57.000Z",
  "updated": "2021-07-25T12:26:57.000Z",
  "title": "A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging",
  "authors": [
    "Aishwarza Panday",
    "Muhammad Ashad Kabir",
    "Nihad Karim Chowdhury"
  ],
  "comment": "23 pages, 6 figures, accepted in Quantitative Biology",
  "journal": "Quantitative Biology, 2021",
  "categories": [
    "cs.CV",
    "cs.LG",
    "eess.IV"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-07-25T12:26:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning techniques",
      "inclusion criteria",
      "reverse transcription-polymerase chain reaction",
      "chest imaging analysis techniques",
      "relevant bibliographic databases"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 23,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}