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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device.

Lu Lou1, Hong Liang1, Zhengxia Wang2

  • 1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

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Summary
This summary is machine-generated.

A novel deep learning model enhances COVID-19 detection accuracy using VGG19 with attention and mixed loss. This efficient method achieves high performance on edge devices, aiding rapid diagnosis.

Keywords:
COVID-19NVIDIA Jetson devicesattention mechanismdeep learningmixed loss

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Science

Background:

  • The COVID-19 pandemic necessitates accurate and accessible diagnostic tools.
  • Existing diagnostic methods face challenges in speed and convenience.
  • Deep learning offers potential for automated medical image analysis.

Purpose of the Study:

  • To develop an improved deep learning model for COVID-19 detection from CT scans.
  • To evaluate the model's performance on resource-constrained edge-computing devices.
  • To enhance diagnostic accuracy and efficiency for COVID-19.

Main Methods:

  • An enhanced VGG19 deep learning model incorporating an attention module and mixed loss was developed.
  • The model was trained and validated on large (COVIDx CT-2A) and medium (integrated CT scan) datasets.
  • Performance was evaluated on embedded NVIDIA Jetson platforms using NVIDIA TensorRT.

Main Results:

  • The improved model achieved high classification accuracy (98.80% and 97.84%) with a six-fold reduction in parameters.
  • On embedded devices, the model maintained 97% accuracy with an inference speed of 0.6-1 FPS.
  • The method demonstrated superior performance compared to existing approaches.

Conclusions:

  • The proposed deep learning method offers a practical and convenient solution for COVID-19 detection.
  • The model is suitable for deployment on low-cost medical edge-computing terminals.
  • The availability of source code facilitates further research and development.