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Related Concept Videos

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Related Experiment Video

Updated: Oct 30, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing

Vipul Kumar Singh1, Maheshkumar H Kolekar1

  • 1Department of Electrical Engineering, Indian Institute of Technology, Patna, India.

Multimedia Tools and Applications
|July 5, 2021
PubMed
Summary

A new deep learning model for COVID-19 diagnosis offers high accuracy (96.40%) and faster results. This optimized model is suitable for mobile devices, improving remote diagnostic capabilities.

Keywords:
COVID-19Chest CT scanDeep LearningDiagnosisEdge ComputingMobileNet V2

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Epidemiology

Background:

  • The COVID-19 pandemic necessitates rapid and accessible diagnostic methods.
  • Current clinical testing for COVID-19 is time-consuming and not widely accessible.
  • Deep learning models show promise for COVID-19 diagnosis but are often too complex for resource-constrained environments.

Purpose of the Study:

  • To develop a computationally efficient deep learning model for COVID-19 diagnosis.
  • To optimize a deep learning model for compatibility with mobile and edge devices.
  • To evaluate the diagnostic performance of the proposed model using chest CT scans.

Main Methods:

  • A fine-tuned deep learning model inspired by MobileNet V2 architecture was developed.
  • The model was optimized for size and complexity for mobile and edge deployment.
  • Extensive experiments were conducted on a dataset of 2482 chest CT scan images.

Main Results:

  • The developed model achieved a high classification accuracy of 96.40%.
  • The model demonstrated a response time approximately ten times shorter than prevailing deep learning models.
  • McNemar's statistical test confirmed the efficacy of the proposed model.

Conclusions:

  • The fine-tuned deep learning model provides an accurate and rapid solution for COVID-19 diagnosis.
  • The model's optimization makes it suitable for remote diagnostic systems on mobile and edge devices.
  • This approach offers a viable alternative to traditional clinical testing, especially in resource-limited settings.