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Related Experiment Video

Updated: Jun 24, 2025

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Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray

Zhen Wang1, Dong Zhao2, Ali Asghar Heidari3

  • 1College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, Jilin, China.

Scientific Reports
|June 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces CAGWOA, an enhanced whale optimization algorithm, for improved COVID-19 diagnosis through multi-threshold image segmentation. The novel method boosts accuracy and efficiency in analyzing lung X-rays.

Keywords:
COVID-19 X-rayMulti-threshold image segmentationSwarm intelligenceWhale optimization algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Image segmentation is crucial for COVID-19 diagnosis, with multi-threshold methods offering efficiency.
  • Existing techniques like Kapur's method and standard Whale Optimization Algorithm (WOA) face limitations in accuracy, stability, and efficiency for COVID-19 segmentation.
  • Challenges include poor stability, low efficiency, and accuracy issues in current threshold selection for COVID-19 detection.

Purpose of the Study:

  • To develop an improved multi-threshold image segmentation technique for COVID-19 diagnosis.
  • To enhance the Whale Optimization Algorithm (WOA) to overcome limitations in stability, efficiency, and accuracy.
  • To introduce a novel algorithm, CAGWOA, for superior segmentation of lung X-ray images.

Main Methods:

  • Introduction of a novel algorithm: Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA).
  • Incorporation of key strategies: COS sampling initialization (COSI) for even solution coverage, adaptive global search (GS) to prevent stagnation, and all-dimensional neighborhood mechanism (ADN) for refined convergence.
  • Validation through benchmark function test sets and comparative experiments on COVID-19 lung X-ray images.

Main Results:

  • CAGWOA demonstrated superior performance compared to existing methods in multi-threshold image segmentation.
  • Experimental results on lung X-ray images showed better image detail preservation and clearer segmentation boundaries.
  • The algorithm exhibited adaptability across different threshold levels, confirming its effectiveness.

Conclusions:

  • CAGWOA significantly improves upon existing methods for COVID-19 diagnosis using multi-threshold image segmentation.
  • The integrated strategies (COSI, GS, ADN) enhance stability, efficiency, and accuracy.
  • CAGWOA offers a promising advancement for medical image analysis in detecting COVID-19.