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Related Experiment Video

Updated: Nov 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network.

Yuqi Fan1,2, Jiahao Liu1,2, Ruixuan Yao1,2

  • 1Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, China.

Pattern Recognition
|June 9, 2021
PubMed
Summary

A novel multi-kernel-size spatial-channel attention method accurately detects COVID-19 from chest X-rays. This approach enhances early diagnosis and isolation, achieving 98.2% accuracy in identifying coronavirus disease 2019 (COVID-19) cases.

Keywords:
AttentionCoronavirusDeep learningMulti-scaleX-ray images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • The rapid global spread of COVID-19 necessitates rapid and accurate diagnostic tools.
  • Chest X-rays exhibit characteristic patterns indicative of COVID-19, crucial for early detection and isolation.
  • Existing automated methods for COVID-19 detection from X-rays often overlook cross-channel and cross-spatial relationships.

Purpose of the Study:

  • To develop an automated method for detecting COVID-19 from chest X-ray images.
  • To address limitations in existing studies by incorporating multi-scope spatial-channel attention mechanisms.
  • To improve the accuracy and efficiency of COVID-19 diagnosis through advanced image analysis.

Main Methods:

  • A three-stage approach involving feature extraction, multi-kernel-size spatial-channel attention, and classification.
  • Utilizing parallel multi-kernel-size spatial and channel attention modules to capture interrelationships across multiple scopes.
  • Employing 1D and 2D convolutional kernels of varying sizes to generate attention feature maps.

Main Results:

  • The proposed method demonstrated improved performance in COVID-19 detection from chest X-ray images.
  • Achieved a high classification accuracy of 98.2% on integrated public datasets.
  • Effectively captured cross-channel and cross-spatial dependencies crucial for accurate diagnosis.

Conclusions:

  • The developed multi-kernel-size spatial-channel attention method is effective for automated COVID-19 detection.
  • This technique offers a promising tool for aiding in the diagnosis and prognosis of COVID-19.
  • The findings highlight the importance of attention mechanisms in medical image analysis for infectious diseases.