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Related Experiment Video

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.

Amirhossein Panahi1, Reza Askari Moghadam1, Mohammadreza Akrami1

  • 1Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

SN Computer Science
|February 28, 2022
PubMed
Summary

This study introduces a deep residual network for diagnosing COVID-19 from chest X-rays. The AI model accurately distinguishes COVID-19 from other pneumonias, aiding radiologists in early detection.

Keywords:
COVID-19Deep learningTransfer learningX-ray images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • COVID-19 pandemic necessitated rapid diagnostic tools.
  • Radiological imaging shows promise for COVID-19 detection.
  • Differentiating COVID-19 from other pneumonias is clinically challenging.

Purpose of the Study:

  • To develop an accurate, automated AI technique for COVID-19 diagnosis using chest X-rays.
  • To enhance the ability to differentiate COVID-19 from viral and bacterial pneumonia.

Main Methods:

  • A deep residual network was developed for analyzing chest X-ray images.
  • The network was trained for binary and multi-class classification tasks.
  • Performance was evaluated against established methods on public datasets.

Main Results:

  • The proposed deep residual network achieved 92.1% accuracy in multi-class classification.
  • The model effectively differentiated COVID-19, normal, viral pneumonia, and bacterial pneumonia cases.
  • The algorithm demonstrated strong performance compared to existing methods.

Conclusions:

  • The AI-driven approach offers a reliable method for COVID-19 detection via chest X-rays.
  • This technique can serve as a valuable tool to support radiologists' diagnostic decisions.
  • Automated analysis of radiological images can aid in managing the COVID-19 pandemic.