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SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural

Aayush Kumar1, Ayush R Tripathi1, Suresh Chandra Satapathy1

  • 1School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha, 751024, India.

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Summary

This study introduces SARS-Net, a deep learning model for COVID-19 detection using Chest X-ray images. SARS-Net achieves high accuracy in identifying viral pneumonia, aiding in rapid population screening.

Keywords:
COVID-19 detectionChest X-rayConvolutional neural networkDeep learningGraph convolutional network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • COVID-19 pandemic necessitates rapid diagnostic tools.
  • Chest X-ray (CXR) imaging shows visual indicators of COVID-19.
  • RT-PCR is accurate but resource-intensive for mass screening.

Purpose of the Study:

  • To develop and evaluate SARS-Net, a novel deep learning model for COVID-19 detection.
  • To leverage Convolutional Neural Networks and Graph Convolutional Networks for CXR analysis.
  • To improve the accuracy and efficiency of COVID-19 diagnosis from medical images.

Main Methods:

  • Developed a custom deep learning architecture named SARS-Net.
  • Combined Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN).
  • Trained and validated the model on Chest X-ray images for COVID-19 diagnosis.

Main Results:

  • SARS-Net achieved 97.60% accuracy on the validation set.
  • The model demonstrated a sensitivity of 92.90% for COVID-19 detection.
  • Quantitative analysis indicated superior performance compared to existing methods.

Conclusions:

  • SARS-Net shows significant potential as a Computer-Aided Diagnosis (CADx) system for COVID-19.
  • The model offers a promising approach for efficient and accurate screening of COVID-19 using CXR.
  • Deep learning models can effectively assist in diagnosing infectious diseases from medical imaging.