Cancer cell detection through histological nuclei images applying the hybrid combination of artificial bee colony and particle swarm optimization algorithms

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dc.contributor.authorAlsarori, Faozia Ali
dc.contributor.authorKaya, Hilal
dc.contributor.authorRahebi, Javad
dc.contributor.authorPopescu, Daniela E.
dc.contributor.authorHemanth, D. Jude
dc.date.accessioned2021-05-15T11:33:55Z
dc.date.available2021-05-15T11:33:55Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionPopescu, Daniela-Elena/0000-0001-7804-5178
dc.description.abstractCancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability. (C) 2020 The Authors. Published by Atlantis Press B.V.en_US
dc.identifier.doi10.2991/ijcis.d.200915.003
dc.identifier.endpage1516en_US
dc.identifier.issn1875-6891
dc.identifier.issn1875-6883
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85092571486
dc.identifier.scopusqualityQ1
dc.identifier.startpage1507en_US
dc.identifier.urihttps://doi.org/10.2991/ijcis.d.200915.003
dc.identifier.urihttps://hdl.handle.net/20.500.12939/251
dc.identifier.volume13en_US
dc.identifier.wosWOS:000608285000014
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorRahebi, Javad
dc.language.isoen
dc.publisherAtlantis Pressen_US
dc.relation.ispartofInternational Journal of Computational Intelligence Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectImage Processingen_US
dc.subjectNuclei Segmentationen_US
dc.subjectPSOen_US
dc.titleCancer cell detection through histological nuclei images applying the hybrid combination of artificial bee colony and particle swarm optimization algorithms
dc.typeArticle

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