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A review of deep learning-based detection methods for COVID-19

  • Nandhini Subramanian
  • , Omar Elharrouss
  • , Somaya Al-Maadeed
  • , Muhammed Chowdhury

Research output: Contribution to journalReview articlepeer-review

Abstract

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.

Original languageEnglish
Article number105233
JournalComputers in Biology and Medicine
Volume143
DOIs
Publication statusPublished - Apr 2022
Externally publishedYes

Keywords

  • COVID-19 detection
  • Coronavirus pandemic
  • DL-Based COVID-19 detection
  • Lung image classification
  • Medical image processing

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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