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Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.

Rana Khattab1, Islam R Abdelmaksoud1, Samir Abdelrazek1

  • 1Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.

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|May 25, 2023
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Summary
This summary is machine-generated.

Deep learning (DL) and artificial intelligence (AI) show promise in detecting COVID-19 using medical imaging. This review analyzes DL models applied to X-ray, CT, and ultrasound for early COVID-19 diagnosis.

Keywords:
Artificial intelligenceCOVID-19Deep learningImaging modalities

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic, caused by SARS-COV-2, has had a profound global impact.
  • Public health measures like lockdowns and curfews were implemented to control the spread.
  • Early and accurate detection of COVID-19 is crucial for effective management and treatment.

Purpose of the Study:

  • To review and analyze deep learning (DL) models for COVID-19 detection using medical imaging.
  • To compare different DL approaches applied to common imaging modalities.
  • To identify future research directions in AI-driven COVID-19 diagnostics.

Main Methods:

  • Systematic review of research studies from January 2020 to September 2022.
  • Focus on deep learning models applied to X-ray, Computed Tomography (CT), and Ultrasound (US) images.
  • Comparative analysis of various DL techniques and their performance in COVID-19 detection.

Main Results:

  • Deep learning models demonstrate significant potential in identifying COVID-19 indicators across X-ray, CT, and US modalities.
  • Various DL architectures and approaches have been explored for COVID-19 detection.
  • The review synthesizes findings on the effectiveness of different DL methods.

Conclusions:

  • Deep learning offers a powerful tool for the rapid and accurate detection of COVID-19 from medical images.
  • Further research is needed to optimize DL models and integrate them into clinical workflows.
  • AI-driven diagnostic tools can play a vital role in combating future pandemics.