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Related Experiment Video

Updated: Jul 29, 2025

High-throughput Detection Method for Influenza Virus
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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization

Himanshu Gupta1, Javed Imran2, Chandani Sharma1

  • 1Department of Computer Science and Engineering, Quantum University, Roorkee, India.

Journal of Ambient Intelligence and Humanized Computing
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces Flu-Net, an AI framework using CCTV footage to detect flu-like symptoms such as coughing and sneezing. The system achieved 70% accuracy, improving early detection and infection control.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • The global COVID-19 pandemic highlights the need for effective disease surveillance.
  • Identifying flu-like symptoms early is crucial for limiting infectious disease spread.

Purpose of the Study:

  • To develop an AI-powered framework, Flu-Net, for recognizing flu-like symptoms from surveillance video.
  • To leverage human action recognition and deep learning for public health monitoring.

Main Methods:

  • Utilized frame differencing to extract foreground motion from CCTV video.
  • Employed a two-stream heterogeneous network (2D/3D ConvNets) for activity recognition.
  • Integrated Grey Wolf Optimization (GWO) for feature selection.
Keywords:
Action recognitionCOVID-19Deep learningGrey Wolf optimizerTwo-stream ConvNet

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Main Results:

  • Flu-Net achieved 70% accuracy in identifying flu-like symptoms.
  • The framework demonstrated an improvement of over 8% compared to baseline methods.
  • Successfully recognized actions like coughing and sneezing in video data.

Conclusions:

  • The proposed Flu-Net framework shows significant potential for real-time public health surveillance.
  • AI-driven analysis of surveillance footage can enhance early detection of infectious disease outbreaks.
  • This approach offers a scalable solution for monitoring public health in real-world settings.