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Related Experiment Video

Updated: Sep 22, 2025

High-throughput Detection Method for Influenza Virus
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Fast COVID-19 versus H1N1 screening using Optimized Parallel Inception.

Alireza Tavakolian1, Farshid Hajati2, Alireza Rezaee1

  • 1Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran.

Expert Systems with Applications
|May 25, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model offers fast screening for COVID-19 and swine-origin influenza A (H1N1) patients, achieving high accuracy. This technology aids in distinguishing between these pandemics with common symptoms.

Keywords:
COVID-19CoronavirusDeep learningH1N1 virusOutbreakScreening

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Area of Science:

  • Computational biology and bioinformatics
  • Infectious disease modeling
  • Artificial intelligence in healthcare

Background:

  • COVID-19 and swine-origin influenza A (H1N1) are global pandemics with overlapping symptoms, complicating clinical diagnosis.
  • Accurate and rapid differentiation between COVID-19 and H1N1 is crucial for effective patient management and public health.
  • Limited research data for specific pandemic strains poses a challenge for developing robust diagnostic models.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for the rapid and accurate screening of COVID-19 and H1N1 patients.
  • To address data scarcity issues in pandemic research using a Semi-supervised Generative Adversarial Network (SGAN).
  • To identify key clinical symptoms and comorbidities differentiating COVID-19 from H1N1.

Main Methods:

  • Introduction of the Optimized Parallel Inception deep learning model for patient screening.
  • Utilization of a Semi-supervised Generative Adversarial Network (SGAN) to augment and balance a combined COVID-19 and H1N1 dataset.
  • Creation of a merged dataset comprising 4,383 COVID-19, 989 H1N1, and 1,059 negative cases for model training and validation.

Main Results:

  • The Optimized Parallel Inception model achieved high screening accuracies of 99.2% for COVID-19 and 99.6% for H1N1.
  • SGAN effectively mitigated issues arising from unequal class densities in the combined pandemic dataset.
  • Analysis identified dry cough, breathing problems, diabetes, and gastrointestinal issues as significant indicators for differentiating COVID-19 and H1N1.

Conclusions:

  • The proposed Optimized Parallel Inception model, enhanced by SGAN, provides a highly accurate and efficient method for distinguishing COVID-19 from H1N1.
  • This deep learning approach offers a valuable tool for healthcare facilities in managing patients during overlapping pandemic outbreaks.
  • Clinical symptom and comorbidity analysis offers insights for improved diagnostic strategies in respiratory pandemics.