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Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network.

Prasad Kalane1, Sarika Patil2, B P Patil3

  • 1Anubhuti Research Centre, Pune, India.

Biomedical Signal Processing and Control
|March 1, 2021
PubMed
Summary

An artificial intelligence tool using U-Net deep learning on CT scans can detect COVID-19. This automated system offers high accuracy for early screening, aiding clinicians in pandemic mitigation efforts.

Keywords:
COVID-19Deep learningRT-PCRSARS-CoV-2U-Net architecture

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, presents challenges with asymptomatic cases and limitations in current RT-PCR testing.
  • Existing diagnostic methods for COVID-19 face issues with kit availability and early-stage symptom detection.

Purpose of the Study:

  • To develop and evaluate an automated system for COVID-19 detection using Artificial Intelligence (AI).
  • To leverage deep learning models, specifically the U-Net architecture, for analyzing Chest CT images.

Main Methods:

  • An automated COVID-19 detection system was designed using the U-Net deep learning architecture.
  • The model was trained and evaluated on a dataset of 1000 Chest CT images from diverse sources.
  • The dataset comprised 552 images from healthy individuals and 448 from COVID-19 patients.

Main Results:

  • The proposed AI system achieved high performance metrics.
  • Sensitivity was 94.86%, specificity was 93.47%, and overall accuracy was 94.10% for COVID-19 detection.
  • The U-Net architecture proved effective for analyzing Chest CT images.

Conclusions:

  • The developed AI-based system demonstrates significant potential for the primary screening of COVID-19.
  • This automated tool can serve as a valuable supplementary resource for clinicians in managing the pandemic.