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Updated: Aug 8, 2025

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Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane.

Abdellah Zakaria Sellam1, Azeddine Benlamoudi2, Clément Antoine Cid3

  • 1Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy.

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Summary
This summary is machine-generated.

This study introduces an automated method for detecting and quantifying SARS-CoV-2 in water samples using smartphone imaging and YOLOv5 AI. This advances environmental surveillance for early outbreak detection.

Keywords:
CSPnetGFCSARS-CoV-2TTAYOLOv5mgLAMP

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Area of Science:

  • Environmental science
  • Virology
  • Computer science

Background:

  • Environmental surveillance of SARS-CoV-2 is crucial for early outbreak detection.
  • Manual quantification of fluorescent virus particles is labor-intensive and prone to error.
  • Existing methods lack efficiency and scalability for large-scale monitoring.

Purpose of the Study:

  • To develop an automated method for detecting and quantifying SARS-CoV-2 fluorescent particles in water.
  • To leverage artificial intelligence (AI) for accurate and efficient cell counting.
  • To enable rapid environmental surveillance using portable technology.

Main Methods:

  • Utilized a membrane-based in-gel loop-mediated isothermal amplification (mgLAMP) for SARS-CoV-2 detection.
  • Employed the YOLOv5 algorithm with CSPnet backbone for object detection.
  • Integrated test time augmentation (TTA) to improve detection of varied cell sizes and shapes.
  • Captured images using a smartphone camera for accessibility.

Main Results:

  • The YOLOv5-based method accurately detected and quantified multiscale and shape-variant SARS-CoV-2 fluorescent cells.
  • Achieved a mean Average Precision (mAP@0.5) score of 90.3% using the YOLOv5-s6 model.
  • Demonstrated the potential for smartphone-based imaging in environmental virus detection.

Conclusions:

  • The proposed AI-driven method offers an efficient, accurate, and scalable solution for environmental SARS-CoV-2 surveillance.
  • Automated analysis significantly reduces the time and expertise required for particle quantification.
  • This approach can enhance early warning systems for potential outbreaks through water monitoring.