Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation

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dc.authorid0000-0003-0069-3479
dc.authorid0000-0002-8531-4054
dc.contributor.authorAydemir, Salih Berkan
dc.date.accessioned2024-11-19T08:28:08Z
dc.date.available2024-11-19T08:28:08Z
dc.date.issuedKasım, 2024
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn neuro-oncology, the precise segmentation of brain tumors from Magnetic Resonance Images is crucial for diagnosis, treatment planning, and monitoring disease progression. Accurate segmentation helps determine the tumor’s size, location, and growth potential, which is essential for formulating effective treatment strategies. In response to this challenge, we developed a novel approach using Chaotic Local Search-Enhanced Differential Evolution (CJADE). CJADE, particularly its variant CJADE-M, which employs chaotic maps selected through a probability-based approach, has proven effective in optimizing brain tumor segmentation. Our study shows that CJADE-M outperforms traditional metaheuristic algorithms on various evaluation metrics. We further enhanced CJADE-M with an entropy-based hybrid objective function, which improved accuracy and reduced computational time in tumor segmentation compared to conventional methods like Minimum Cross-Entropy and Kapur. This makes our method suitable for real-time medical imaging analysis. Our findings indicate that CJADE-M, equipped with the hybrid objective function, achieves superior segmentation performance for both benign lobulated and malignant irregular tumors across metrics such as PSNR, FSIM, QILV, and HPSI. By providing a more accurate and efficient tool, our approach can significantly enhance the outcomes of brain tumor diagnosis and treatment, improving patient care in neuro-oncology.
dc.identifier.citationAydemir, S. B., Onay, F. K., & Yalcin, E. (2024). Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation. Biomedical Signal Processing and Control, 96, 106631.
dc.identifier.doi10.1016/j.bspc.2024.106631
dc.identifier.issuePart A
dc.identifier.scopusqualityQ1
dc.identifier.startpage106631
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30876
dc.identifier.volume96
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorYalcin, Emre
dc.institutionauthorid0000-0003-3818-6712
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDifferential evolution
dc.subjectHybrid objective function
dc.subjectBrain tumor segmentation
dc.subjectMetaheuristic optimization
dc.subjectChaotic maps
dc.titleEmpowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation
dc.typeArticle

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