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Updated: Aug 22, 2025

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An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.

Weiping Ding1, Shouvik Chakraborty2, Kalyani Mali2

  • 1School of Information Science and TechnologyNantong University Nantong 226019 China.

IEEE Transactions on Fuzzy Systems : a Publication of the IEEE Neural Networks Council
|November 8, 2022
PubMed
Summary

This study introduces a novel method using fuzzy clustering and superpixels for early COVID-19 diagnosis from radiological images. The approach shows promising results for real-life applications in pandemic scenarios.

Keywords:
COVID-19interval type-2 fuzzy systemradiological image segmentationunsupervised fuzzy clustering

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • The global COVID-19 pandemic necessitates rapid diagnostic tools.
  • Early diagnosis of COVID-19 is crucial for effective management and prevention.
  • Lack of specific antiviral drugs highlights the need for supportive and preventative measures.

Purpose of the Study:

  • To develop an automated method for early COVID-19 diagnosis using radiological images.
  • To enhance the efficiency and accuracy of image segmentation for diagnostic purposes.
  • To leverage advanced computational techniques for medical image analysis.

Main Methods:

  • Integration of interval type 2 fuzzy clustering with superpixels and metaheuristics for image segmentation.
  • Utilizing a modified watershed-based approach for superpixel computation, with noise reduction via morphological operations.
  • Enhancement of the fuzzy c-means algorithm by incorporating spatial information from local windows.
  • Optimization of clustering using a modified flower pollination algorithm.

Main Results:

  • The proposed method demonstrates efficient segmentation of radiological images.
  • Experimental results indicate promising performance for real-world deployment.
  • The approach effectively incorporates spatial information, reducing computational load.

Conclusions:

  • The developed image analysis technique shows potential for early COVID-19 detection.
  • This method can serve as a valuable supportive tool in pandemic situations.
  • Further validation is recommended, though the approach is not a substitute for gold-standard tests.