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COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction.

Sanjoy Chakraborty1, Apu Kumar Saha2, Sukanta Nama3

  • 1Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura, India.

Computers in Biology and Medicine
|November 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a modified whale optimization algorithm (mWOAPR) for rapid and accurate COVID-19 severity assessment from X-ray images. The enhanced algorithm improves diagnostic accuracy, aiding in timely patient treatment.

Keywords:
COVID-19 chest X-ray imageImage segmentationKapur's entropyMultilevel thresholdingWhale optimization algorithm

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Coronavirus disease 2019 (COVID-19) significantly impacts global health and necessitates rapid diagnostic tools.
  • Accurate interpretation of chest X-ray images is crucial for timely COVID-19 diagnosis and treatment planning.
  • Existing diagnostic methods require enhancement for speed and precision in the context of the pandemic.

Purpose of the Study:

  • To develop a computational tool for rapid and accurate COVID-19 severity assessment using chest X-ray images.
  • To improve diagnostic accuracy through a modified whale optimization algorithm (WOA).
  • To enhance the WOA's global search and exploitation capabilities for image segmentation.

Main Methods:

  • A modified whale optimization algorithm with population reduction (mWOAPR) was developed.
  • The algorithm incorporates random population initialization and adjusted parameters (A, b) for improved exploration and exploitation.
  • The mWOAPR method was applied to segment benchmark images and COVID-19 chest X-ray images using multilevel thresholding and Kapur's entropy.

Main Results:

  • The mWOAPR method demonstrated improved performance in segmenting benchmark images compared to basic and modified metaheuristic algorithms.
  • Segmentation of COVID-19 chest X-ray images using mWOAPR showed enhanced accuracy.
  • Comparative analysis confirmed the superior performance of the proposed mWOAPR over existing methods.

Conclusions:

  • The developed mWOAPR algorithm offers a promising computational tool for efficient COVID-19 diagnosis from X-ray images.
  • The modified optimization technique enhances the accuracy and speed of medical image analysis.
  • This approach has the potential to significantly aid healthcare professionals in managing COVID-19 cases.