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Related Experiment Video

Updated: Oct 22, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks.

Sherif Elbishlawi1, Mohamed H Abdelpakey1, Mohamed S Shehata1

  • 1Department of Computer Science, Math, Physics, and Statistics, The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces CORONA-Net, a novel Convolutional Neural Network (CNN) for accurate COVID-19 detection from chest X-rays. CORONA-Net achieves 95.84% accuracy, aiding early disease identification.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
  • Accurate testing for COVID-19 remains a significant challenge.
  • Convolutional Neural Networks (CNNs) offer potential for automated analysis of medical images.

Purpose of the Study:

  • To develop and evaluate a novel CNN model, CORONA-Net, for detecting COVID-19 from chest X-ray images.
  • To improve the accuracy and efficiency of COVID-19 diagnosis through an automated system.
  • To provide radiologists with a tool for validating their diagnostic findings.

Main Methods:

  • A two-phase CNN architecture, CORONA-Net, was designed, comprising a reinitialization phase (encoder-decoder) and a classification phase (encoder backbone).
Keywords:
CORONA-NetCOVID-19deep learningpandemic

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  • The reinitialization phase trained encoder and decoder networks using medical image distributions.
  • The classification phase fine-tuned the encoder network using weights from the reinitialization phase.

    • Main Results:

    • CORONA-Net achieved a high overall accuracy of 95.84% in detecting COVID-19 from chest X-rays.
    • The proposed network demonstrated superior performance compared to existing state-of-the-art methods.
    • Extensive experiments were conducted on the publicly available COVIDx dataset.

    Conclusions:

    • CORONA-Net is a highly accurate and effective deep learning model for COVID-19 detection using chest X-rays.
    • The two-phase approach of CORONA-Net enhances diagnostic capabilities.
    • This automated system shows promise in combating the COVID-19 pandemic through improved early detection.