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Performance optimization of hunger games search for multi-threshold COVID-19 image segmentation

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Abstract

COVID-19 X-ray images are a vital approach for diagnosing whether a patient has an infection. By using multi-threshold image segmentation (MIS) technology to segment the target area of the COVID-19 X-ray image automatically, doctors can more efficiently determine whether the patient is infected with the virus and the current course of the disease. Nevertheless, as the threshold value rises, the computing cost of MIS approaches grows considerably, and for this reason, many researchers utilize meta-heuristic algorithms (MAs) as optimizers to select the optimal thresholds. Yet some issues cause slow convergence and local optimum solutions stalling. To revise the drawbacks, this paper proposes a strengthened version of the hunger games search (HGS), titled CDHGS. CDHGS introduces crisscross optimizer (CSO) and dimension learning-based hunting (DLH) mechanisms to HGS. First, CSO allows different individuals to exchange information, which speeds up convergence. Then, DLH mines more details on an individual's surrounding neighbors, thus alleviating the local optimum problem of the algorithm. A series of comparative experiments completed at CEC2014 showed that the proposed CDHGS has superior performance in respect of optimization than other advanced algorithms. Besides, a CDHGS-based MIS method is presented and employed to segment COVID-19 X-ray images. Specifically, we build a two-dimensional (2D) histogram utilizing non-local mean and grayscale images to illustrate the information of images, use Rényi's entropy as the objective function, and maximize Rényi's entropy to find the optimal thresholds. The COVID-19 X-ray image segmentation (IS) results of the evaluation display that the CDHGS-based MIS can obtain considerably exceptional segmentation results and stronger robustness than other segmentation methods. In all, CDHGS is a competitive approach in both global optimization (GO) and IS.

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Data availability

The data involved in this study are all public data, which can be downloaded through public channels.

Abbreviations

MIS:

Multi- threshold image segmentation

CDHGS:

The developed HGS in this paper

NL-means:

Non-local means

Y i :

Pixel i’s greyscale value of image Y

J i :

The non-local means

B(i):

The normalization constants

W(i, j):

The similarity of pixels j and i

μ(i):

The local means

I i :

n × n blocks of images around pixels i

α :

Standard deviation

L:

The grey level

p(i, j):

The grayscale probability density

h :

Filtering’s degree

H(i, j):

The frequency of occurrence of the group (i, j)

N :

Population size

D :

Data dimension

FEs :

The current number of evaluations

MaxFEs :

The maximum number of evaluations

CSO:

Crisscross optimizer

DLH:

Dimension learning-based hunting

VCS:

Vertical crossover search

HCS:

Horizontal crossover search

X i :

The ith individual of the population

X b :

The optimal solution for the population

E :

A variant in the range of [0,1]

F(i):

The fitness value of ith individual

BF :

The best fitness value

WF :

The worst fitness value

hungry(i):

The ith individual’s hunger

SHungry :

The sum hunger of all individuals

AllFitness(i):

The sum of the fitness values of all individuals

H :

Hunger sensation

LH :

The lower bound of H

MS _ H :

The descendants of individuals by HCS

MS _ V :

The descendants of individuals by VCS

X i _ HGS :

New candidate generated by HGS

X i _ DLH :

New candidate generated by DLH

R i :

The Euclidean distance between Xi and Xi _ HGS

Nr i :

Neighbors of the ith individual

X n :

A random neighbor from Nri

X r :

A randomly selected individual

Dis i :

The Euclidean distance between Xi and Xj

References

  1. Abd Elaziz M et al (2021) Automatic superpixel-based clustering for color image segmentation using q-generalized pareto distribution under linear normalization and hunger games search. Mathematics 9. https://doi.org/10.3390/math9192383

  2. Abdulkareem KH et al (2022) Automated system for identifying COVID-19 infections in computed tomography images using deep learning models. J Healthc Eng 2022:5329014

  3. Abutaleb AS (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput Vis, Graph, Image Proc 47(1):22–32

    Article  Google Scholar 

  4. Ahmadianfar I et al (2021) RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications 181:115079

  5. Ahmadianfar I et al (2022) INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:116516

  6. Aja-Fernandez S et al (2006) Image quality assessment based on local variance. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society

  7. Allioui H et al (2022) A multi-agent deep reinforcement learning approach for enhancement of COVID-19 CT image segmentation. J Pers Med 12:309. https://doi.org/10.3390/jpm12020309.

  8. Althubiti S et al (2022) Improved metaheuristics with machine learning enabled medical decision support system. Comput, Mat Cont 73(2):2423–2439

    Google Scholar 

  9. Appathurai A et al (2019) Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images. IEEE Access:89570-89580

  10. Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In Proceedings of the IEEE swarm intelligence symposium, Indianapolis, IN, USA. 2006, p 12-14

  11. Borjigin S, Sahoo PK (2019) Color image segmentation based on multi-level Tsallis–Havrda–Charvát entropy and 2D histogram using PSO algorithms. Pattern Recognit 92:107-118

  12. Cai Z et al (2019) Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl 138: 112814

  13. Cai W et al (2021) Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation. Displays 70:102106

  14. Chen X et al (2017) Biogeography-based learning particle swarm optimization. Soft Comput 21:7519–7541

  15. Chen P et al (2020) Harnessing semantic segmentation masks for accurate facial attribute editing. Concurrency and Computation: Practice and Experience Vol. 34

  16. Chen H et al (2020) An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Syst Appl 154:113018

  17. Coll B, Morel J-M (2005) A non-local algorithm for image denoising 2:60-65

  18. Coll B, Morel J-M (2005) A review of image denoising algorithms with a new one. SIAM Journal on Multiscale Modeling and Simulation 4(2):490–530

  19. De Albuquerque MP et al (2004) Image thresholding using Tsallis entropy. Pattern Recognit Lett 25:1059-1065

  20. Derrac J et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18

    Article  Google Scholar 

  21. Devi RM et al (2022) BHGSO: Binary hunger games search optimization algorithm for feature selection problem. Comput Mater Contin 70(1)

  22. Dong B, Jin R, Weng G (2019) Active contour model based on local bias field estimation for image segmentation. Signal Process Image Commun 78:187-199

  23. Elsisi M, Essa ME-SM (2022) Improved bald eagle search algorithm with dimension learning-based hunting for autonomous vehicle including vision dynamics. Appl Intell 53(10):11997–12014

  24. Fan Y et al (2021) A bioinformatic variant fruit fly optimizer for tackling optimization problems. Knowledge-Based Systems 213:106704

  25. Feng Q, Feng Z, Su X (2021) Design and simulation of human resource allocation model based on double-cycle neural network. Comput Int Neurosci 2021:1-10

  26. García S et al (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064

    Article  Google Scholar 

  27. García-Martínez C et al (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Operation Res 185:1088-1113

  28. Gupta S, Deep K (2020) A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Appl Intell 50(4):993–1026

  29. Hasoon JN et al (2021) COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. Results Phys 31:105045

  30. Heidari AA et al (2019) Harris hawks optimization: Algorithm and applications. Future Gener Comput Syst 97:849-872

  31. Heidari AA, Ali Abbaspour R, Chen H (2019) Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Appl Soft Comput  81:105521

  32. Hemdan EE-D, Shouman M, Karar M (2020) COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in x-ray images. ArXiv abs/2003.11055

  33. Hongwei L et al (2019) Chaos-enhanced moth-flame optimization algorithm for global optimization. J Syst Eng Electron 30(6):1144–1159

    Article  Google Scholar 

  34. Hu F et al (2021) Is health contagious?—Based on empirical evidence from China family panel studies' data. Front Publ Health 9:691746

  35. Hu J et al (2021) Chaotic diffusion-limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection (Intelligent Systems, impact factor:8.709). Int J Intell Syst. https://doi.org/10.1002/int.22744

  36. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image/video quality assessment. Electron Lett 44:800-801

  37. Ibrahim DA et al (2022) Effective hybrid deep learning model for COVID-19 patterns identification using CT images. Expert Syst e13010

  38. Ismael AM, Şengür A (2020) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl 164:114054

  39. Jin K et al (2023) iERM: An interpretable deep learning system to classify epiretinal membrane for different optical coherence tomography devices: A Multi-Center Analysis. J Clin Med 12(2):400

    Article  PubMed  PubMed Central  Google Scholar 

  40. Jun LUO, Yongsong Y, Baoyu SHI (2019) Multi-threshold image segmentation of 2D Otsu based on improved adaptive differential evolution algorithm. J Electron Inf Technol 41(8):2017–2024

    Google Scholar 

  41. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis, Graph, Image Proc 29(3):273–285

    Article  Google Scholar 

  42. Kaushal C et al (2022) A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks. Comput Intell Neurosci 2022:7935346

  43. Kumar M, Dhillon JS (2019) A conglomerated ion-motion and crisscross search optimizer for electric power load dispatch. Appl Soft Comput 83:105641

  44. Kutlu Onay F, Aydemir SB (2022) Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Math Comput Simul 192:514-536

  45. Li Y et al (2014) Multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising. Nat Comput 13(1):39–53

    Article  ADS  MathSciNet  Google Scholar 

  46. Li Q et al (2020) Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord 277:153-158

  47. Li S et al (2020) Slime mould algorithm: A new method for stochastic optimization. Future Gener Comput Syst 111:300-323

  48. Li S et al (2020) Multi-angle head pose classification when wearing the mask for face recognition under the COVID-19 coronavirus epidemic. In: 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

  49. Li C et al (2021) Memetic Harris Hawks Optimization: Developments and perspectives on project scheduling and QoS-aware web service composition. Expert Syst Appl 171:114529

  50. Li B et al (2023) Image Colorization using CycleGAN with semantic and spatial rationality. Multimed Tools Appl 82(14):21641–21655

  51. Li M et al (2023) Power normalized cepstral robust features of deep neural networks in a cloud computing data privacy protection scheme. Neurocomputing 518:165-173

  52. Liang J, Qu B, Suganthan P (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, ed. CEC2014

  53. Liang R, Le-Hung T, Nguyen-Thoi T (2022) Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. J Build Eng 59:105087

  54. Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory 37:145-151

  55. Liu Y et al (2020) Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models. Energy Convers Manag 223:113211

  56. Liu G et al (2020) Predicting cervical hyperextension injury: A covariance guided sine cosine support vector machine. IEEE Access 8:46895-46908

  57. Liu L et al (2021) Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 138:104910

  58. Liu R et al (2021) SCCGAN: style and characters inpainting based on CGAN. Mob Netw Appl 26:3-12

  59. Liu S et al (2022) 2D/3D multimode medical image registration based on normalized cross-correlation. Appl Sci 12(6):2828

    Article  ADS  CAS  Google Scholar 

  60. Ma BJ, Liu S, Heidari AA (2022) Multi-strategy ensemble binary hunger games search for feature selection. Knowledge-Based Systems 248:108787

  61. Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441-453

  62. Maguolo G, Nanni L (2020) A critic evaluation of methods for COVID-19 automatic detection from X-Ray images. arXiv

  63. Mahmoudi R et al (2022) A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging. Appl Sci 12

  64. Mansour RF et al (2021) Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification. Patt Recog Lett 151:267-274

  65. Michetti J et al (2015) Influence of CBCT parameters on the output of an automatic edge-detection-based endodontic segmentation. Dento maxillo Fac Radiolog 44(8):20140413

    Article  CAS  Google Scholar 

  66. Mijwil MM (2021) Implementation of machine learning techniques for the classification of lung x-ray images used to detect COVID-19 in humans. Iraqi J Sci 62(6):2099–2109

    Article  Google Scholar 

  67. Mijwil MM (2022) Has the future started? The current growth of artificial intelligence, machine learning, and deep learning. Iraqi Journal for Computer Science and Mathematics

  68. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Soft 95:51-67

  69. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Soft 69:46-61

  70. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Comput & Applic 27(2):495–513

    Article  Google Scholar 

  71. Mirjalili S et al (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Soft 114:163-191

  72. Mirjalili S, Dong JS, Lewis A (2019) Nature-inspired optimizers: theories, literature reviews and applications. Vol. 811. 2019: Springer

  73. Mittal H et al (2021) Gravitational search algorithm: a comprehensive analysis of recent variants. Multimed Tools Appl 80(5):7581–7608

    Article  Google Scholar 

  74. Mohammed MA et al (2022) Novel crow swarm optimization algorithm and selection approach for optimal deep learning COVID-19 diagnostic model. Comput Intell Neurosci 2022:1307944

  75. Mousavirad SJ et al (2022) Population-based self-adaptive Generalised Masi Entropy for image segmentation: A novel representation. Knowl-Based Syst 245:108610

  76. Mugemanyi S et al (2020) Optimal reactive power dispatch using chaotic bat algorithm. IEEE Access 8:65830–65867

  77. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

  78. Nagi AT et al (2022) Performance analysis for COVID-19 diagnosis using custom and state-of-the-art deep learning models. Appl Sci 2022:12. https://doi.org/10.3390/app12136364.

  79. Namburu A, Srinivas Kumar S, Srinivasa Reddy E (2022) Review of set theoretic approaches to magnetic resonance brain image segmentation. IETE J Res 68(1):350–367

    Article  Google Scholar 

  80. Narappanawar N, Rao M, Joshi M (2011) Graph theory based segmentation of traced boundary into open and closed sub-sections. Comput Vis Image Underst 115:1552-1558

  81. Nenavath H, Kumar Jatoth DR, Das DS (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43:1-30

  82. Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

  83. Otsu N (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transac Syst, Man, Cyberne 9(1):62–66

    Article  Google Scholar 

  84. Pare S et al (2020) Image segmentation using multilevel thresholding: A research review. Iran J Sci Technol, Transac Elect Eng 44:1-29

  85. Parikh BH et al (2022) A bio-functional polymer that prevents retinal scarring through modulation of NRF2 signalling pathway. Nat Commun 13(1):2796

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  86. Preeti RK, Singh D (2022) Dimension learning based chimp optimizer for energy efficient wireless sensor networks. Sci Rep 12(1):14968

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  87. Qi A et al (2022) Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput Biol Med 148:105810

  88. Qu C et al (2018) A modified sine-cosine algorithm based on neighborhood search and greedy levy mutation. Computational Intelli Neurosci 2018:1-19

  89. Reisenhofer R et al (2018) A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Process Image Commun 61:33-43

  90. Rényi A (1961) On measures of entropy and information. In: Symposium on mathematics statistics and probabilities, pp 547–561

  91. Shamim S et al (2022) Automatic COVID-19 lung infection segmentation through modified unet model. J Healthc Eng 2022:6566982

  92. Shirly S, Ramesh K (2019) Review on 2D and 3D MRI image segmentation techniques. Curr Med Imag Rev 15(2):150–160

    Article  CAS  Google Scholar 

  93. Singh RP, Mukherjee V, Ghoshal SP (2016) Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem. Appl Soft Comput 40:161-177

  94. Storn R, Price KV (1997) Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341-359

  95. Su H et al (2022) A horizontal and vertical crossover cuckoo search: Optimizing performance for the engineering problems. J Comput Des Eng 10(1):36–64

  96. Sun T-Y et al (2011) Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions. IEEE Trans Evol Comput 15:798-811

  97. Tian J et al (2022) Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems. Complex Intell Syst:1–49

  98. Tsai C-Y, Liu T-Y, Chen W-C (2012) A novel histogram-based multi-threshold searching algorithm for multilevel colour thresholding. Int J Adv Robot Syst 9(5):223

    Article  Google Scholar 

  99. Upadhyay P, Chhabra JK (2021) Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm. J Ambient Intell Humaniz Comput 12(1):1081–1098

    Article  Google Scholar 

  100. Venter G, Jaroslaw S-S (2002) Particle swarm optimization. AIAA Journal 41

  101. Vijh S, Saraswat M, Kumar S (2022) Automatic multilevel image thresholding segmentation using hybrid bio-inspired algorithm and artificial neural network for histopathology images. Multimed Tools Appl 82(4):4979–5010

  102. Wang Z et al (2014) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Proc 13:600-612

  103. Wang W, Chen Z, Yuan X (2022) Simple low-light image enhancement based on Weber–Fechner law in logarithmic space. Signal Process Image Commun 106:116742

  104. Wang X et al (2022) An enhanced priori knowledge GAN for CT images generation of early lung nodules with small-size labelled samples. Oxid Med Cell Long 2022

  105. Wolpert D, Macready W, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82. Evolutionary Computation, IEEE Transactions on 1:67-82

  106. Xia J et al (2022) Adaptive barebones salp swarm algorithm with quasi-oppositional learning for medical diagnosis systems: A comprehensive analysis. J Bionic Eng 19(1):240–256

    Article  MathSciNet  Google Scholar 

  107. Xu Y et al (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inform Sci 492:181-203

  108. Xu Y et al (2019) An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 129:135–155

  109. Xu Z et al (2020) Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Med 15(1):860–871

    Article  Google Scholar 

  110. Xu B et al (2022) Quantum nelder-mead hunger games search for optimizing photovoltaic solar cells. Int J Energ Res. https://doi.org/10.1002/er.8011

  111. Yang X-S (2010) A new metaheuristic bat-inspired algorithm 284

  112. Yang Y, Yan H (2000) An adaptive logical method for binarization of degraded document images. Pattern Recogn 33(5):787–807

    Article  ADS  Google Scholar 

  113. Yang Y et al (2021) Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864

  114. Yang D et al (2022) LFRSNet: a robust light field semantic segmentation network combining contextual and geometric features. Frontiers in Environmental Science

  115. Yin P-Y (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184(2):503–513

    MathSciNet  Google Scholar 

  116. Zhang L et al (2011) FSIM: A Feature SIMilarity index for image quality assessment. Image Processing, IEEE Transactions on 20:2378-2386

  117. Zhang Q et al (2019) Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access 7:31243-31261

  118. Zhang Q et al (2021) Gaussian barebone salp swarm algorithm with stochastic fractal search for medical image segmentation: A COVID-19 case study. Comput Biol Med 139:104941

  119. Zhang C et al (2023) Improved hybrid grey wolf optimization algorithm based on dimension learning-based hunting search strategy. IEEE Access 11:13738-13753

  120. Zhao S et al (2021) Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy. Comput Biol Med 139:105015

  121. Zhao D et al (2021) Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation. Expert Syst Appl 167:114122

  122. Zhao S et al (2021) Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease. Comput Bio Med 134:104427

  123. Zhou W, Bovik AC (2002) A universal image quality index. IEEE Signal Proc Lett 9(3):81–84

    Article  ADS  Google Scholar 

  124. Zhu B et al (2018) Hilbert spectra and empirical mode decomposition: a multiscale event analysis method to detect the impact of economic crises on the European carbon market. Comput Econ 52(1):105–121

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Acknowledgments

The authors extend their appreciation to the journal editor and reviewers for their comments. This work was supported in part by the Natural Science Foundation of Zhejiang Province (LZ22F020005), National Natural Science Foundation of China (62076185).

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  1. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Shuhui Hao, Changcheng Huang, Qike Shao & Huiling Chen

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

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Hao, S., Huang, C., Heidari, A. et al. Performance optimization of hunger games search for multi-threshold COVID-19 image segmentation. Multimed Tools Appl 83, 24005–24044 (2024). https://doi.org/10.1007/s11042-023-16116-z

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