An improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor images

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dc.authorscopusid43361385400en_US
dc.authorscopusid57219716093en_US
dc.authorscopusid57201815384en_US
dc.authorscopusid55326012400en_US
dc.authorscopusid55650519900en_US
dc.authorscopusid55354654200en_US
dc.contributor.authorHoussein, Essam H.
dc.contributor.authorEmam, Marwa M.
dc.contributor.authorSingh, Narinder
dc.contributor.authorSamee, Nagwan Abdel
dc.contributor.authorAlabdulhafith, Maali
dc.contributor.authorCelik, Emre
dc.date.accessioned2024-08-23T16:07:10Z
dc.date.available2024-08-23T16:07:10Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractGlobal optimization and biomedical image segmentation are crucial in diverse scientific and medical fields. The Honey Badger Algorithm (HBA) is a newly developed metaheuristic that draws inspiration from the foraging behavior of honey badgers. Similar to other metaheuristic algorithms, HBA encounters difficulties associated with exploitation, being trapped in local optima, and the pace at which it converges. This study aims to improve the performance of the original HBA by implementing the Enhanced Solution Quality (ESQ) method. This strategy helps to prevent becoming stuck in local optima and speeds up the convergence process. We conducted an assessment of the enhanced algorithm, mHBA, by utilizing a comprehensive collection of benchmark functions from IEEE CEC'2020. In this evaluation, we compared mHBA with well-established metaheuristic algorithms. mHBA demonstrates exceptional performance, as shown by both qualitative and quantitative assessments. Our study not only focuses on global optimization but also investigates the field of biomedical image segmentation, which is a crucial process in numerous applications involving digital image analysis and comprehension. We specifically focus on the problem of multi-level thresholding (MT) for medical image segmentation, which is a difficult process that becomes more challenging as the number of thresholds needed increases. In order to tackle this issue, we suggest a revised edition of the standard HBA, known as mHBA, which utilizes the ESQ approach. We utilized this methodology for the segmentation of Magnetic Resonance Images (MRI). The evaluation of mHBA utilizes existing metrics to gauge the quality and performance of its segmentation. This evaluation showcases the resilience of mHBA in comparison to many established optimization algorithms, emphasizing the effectiveness of the suggested technique.en_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R407), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. [PNURSP2024R407]; Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabiaen_US
dc.description.sponsorshipThe authors would like to express their grateful to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R407), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.DAS:The datasets provided during the current study are available: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427 and https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri.en_US
dc.identifier.doi10.1007/s10586-024-04525-0
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.scopus2-s2.0-85199005453en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10586-024-04525-0
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14529
dc.identifier.wosWOS:001272289800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMetaheuristicsen_US
dc.subjectHoney badger algorithm (HBA )en_US
dc.subjectEnhance solution quality (ESQ)en_US
dc.subjectMulti-level thresholdingen_US
dc.subjectImage segmentationen_US
dc.subjectMoth-Flame Optimizationen_US
dc.subjectSelectionen_US
dc.subjectEntropyen_US
dc.subjectOtsuen_US
dc.titleAn improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor imagesen_US
dc.typeArticleen_US

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