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Updated: Oct 26, 2025

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Applying deep learning-based multi-modal for detection of coronavirus.

Geeta Rani1, Meet Ganpatlal Oza1, Vijaypal Singh Dhaka1

  • 1Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan India.

Multimedia Systems
|July 26, 2021
PubMed
Summary

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This study developed a deep learning model for COVID-19 screening using chest X-rays and genomic data. The model accurately detects SARS-CoV-2 genomes and classifies radiographs, aiding in rapid diagnosis and potential drug discovery.

Area of Science:

  • Medical Imaging
  • Genomics
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic necessitated rapid diagnostic tools.
  • Chest radiographs and genomic data are crucial for identifying SARS-CoV-2.

Purpose of the Study:

  • To design a deep learning multi-modal system for COVID-19 screening.
  • To analyze genomic similarity between SARS-CoV-2 and other viruses.
  • To provide a tool for rapid classification of infected and non-infected individuals.

Main Methods:

  • Developed a deep learning multi-modal approach.
  • Utilized chest radiographs and genomic sequences for analysis.
  • Trained and validated models on public datasets (NCBI, GitHub, Kaggle).
Keywords:
CNNCOVID-19Deep learningDrugGenome matchingSARS-CoV-2

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Main Results:

  • Achieved 99.27% accuracy in detecting SARS-CoV-2 genomes.
  • Demonstrated 95.47% sensitivity in screening chest radiographs for COVID-19, non-COVID pneumonia, and healthy cases.
  • Identified genomic similarity among coronaviruses and other viruses.

Conclusions:

  • The deep learning model is effective for rapid COVID-19 screening using multi-modal data.
  • The tool can assist clinicians in quick diagnosis and potentially guide drug discovery efforts.