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Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

Wei Wang1, Yongbin Jiang1, Xin Wang1

  • 1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.

BMC Medical Imaging
|July 30, 2022
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Summary
This summary is machine-generated.

A new MLES-Net deep learning model accurately diagnoses COVID-19 from X-ray images. This convolutional neural network, MLES-Net56-GAPFC, achieved 100% accuracy for COVID-19 detection, offering a practical solution for timely diagnosis.

Keywords:
COVID-19Chest X-Ray imagesConvolutional neural network (CNN)Deep learningMLES-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Corona Virus Disease 2019 (COVID-19) is a highly infectious pneumonia that emerged in December 2019.
  • The rapid global spread and high mortality rate of COVID-19 necessitate accurate and timely diagnostic methods.
  • Traditional diagnostic methods face challenges in rapid and widespread application, highlighting the need for advanced solutions.

Purpose of the Study:

  • To introduce a novel Multi-Level Enhanced Sensation (MLES) module for improved feature extraction.
  • To propose a new convolutional neural network, MLES-Net, designed for enhanced COVID-19 diagnosis.
  • To evaluate the effectiveness of MLES-Net in accurately identifying COVID-19 from medical images.

Main Methods:

  • The MLES module was developed to automatically focus on key features in medical images.
  • Attention mechanisms were employed to generate attention masks by correlating global and local features.
  • The MLES-Net model was trained and tested using different classifiers, including FC, GAP, and GAPFC modules.

Main Results:

  • The MLES-Net56-GAPFC model achieved an overall accuracy of 95.27%.
  • The MLES-Net56-GAPFC model demonstrated a 100% recognition rate specifically for the COVID-19 category.
  • The GAPFC classifier provided the best balance of parameters, computation, and detection accuracy.

Conclusions:

  • The MLES-Net56-GAPFC model exhibits strong classification capabilities, even with high similarity between COVID-19 categories and low intra-class variability in X-ray images.
  • The proposed MLES-Net56-GAPFC offers a practical and efficient solution for COVID-19 diagnosis.
  • The model's performance indicates its potential for widespread clinical application in combating the COVID-19 pandemic.