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Related Experiment Video

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Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images.

Shuhui Hao1, Changcheng Huang1, Ali Asghar Heidari1

  • 1Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China.

Biomedical Signal Processing and Control
|June 26, 2023
PubMed
Summary

This study introduces an enhanced Firefly Optimization Algorithm (EEFOA) for segmenting COVID-19 X-ray images. EEFOA improves diagnostic accuracy by effectively segmenting images, aiding rapid detection of the disease.

Keywords:
COVID-19 X-ray imagesFruit fly optimization algorithmMeta-heuristic algorithmMulti-threshold image segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • COVID-19 poses a significant global health threat.
  • Accurate segmentation of COVID-19 X-ray images is crucial for timely diagnosis.
  • Existing segmentation methods may face challenges with accuracy and robustness.

Purpose of the Study:

  • To develop an improved optimization algorithm for enhanced medical image segmentation.
  • To apply the proposed algorithm to the specific task of segmenting COVID-19 X-ray images.
  • To evaluate the algorithm's performance against existing methods.

Main Methods:

  • A modified Firefly Optimization Algorithm (EEFOA) was developed, incorporating Elite Natural Evolution (ENE) and Elite Random Mutation (ERM).
  • The EEFOA algorithm was benchmarked against original FOA, its variants, and advanced algorithms on the CEC2014 dataset.
  • EEFOA was applied to Multi-Threshold Image Segmentation (MIS) of COVID-19 X-ray images using a 2D histogram and Rényi's entropy.

Main Results:

  • EEFOA demonstrated superior performance and faster convergence compared to other algorithms in benchmark tests.
  • Experimental results confirmed EEFOA's effectiveness in speeding up convergence and avoiding local optima.
  • EEFOA achieved higher quality and more robust segmentation results for COVID-19 X-ray images compared to advanced methods.

Conclusions:

  • The proposed EEFOA algorithm offers a significant advancement in optimizing image segmentation tasks.
  • EEFOA provides a robust and accurate solution for segmenting COVID-19 X-ray images, supporting clinical diagnosis.
  • The enhanced optimization strategies in EEFOA effectively address limitations of traditional algorithms.