Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2408
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dc.contributor.authorİnik, Özkan-
dc.contributor.authorÜlker, Erkan-
dc.date.accessioned2022-05-23T20:22:41Z-
dc.date.available2022-05-23T20:22:41Z-
dc.date.issued2022-
dc.identifier.issn1432-7643-
dc.identifier.issn1433-7479-
dc.identifier.urihttps://doi.org/10.1007/s00500-021-06711-3-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2408-
dc.description.abstractThe use of deep learning models has become widespread in different computer vision problems such as classification, detection, and segmentation. Many deep learning models have been developed in the segmentation of medical images. Although segmentation accuracy has been increased, segmentation performance needs to be improved due to the variability of tissue, cell and image acquisition methods. In the deep-learning-based segmentation and classification methods, the parameters of the method should be optimized in order to obtain more successful results for segmentation. In this study, the optimization of the parameters has been performed with five optimization algorithms according to segmentation loss. These algorithms are Grey Wolf Optimizer, Artificial Bee Colony (ABC), Genetic Algorithm, Particle Swarm Optimization (PSO), and Black Widow Optimization (BWO). In the experimental studies, each algorithm was run independently ten times and ABC obtained the lowest average segmentation loss with a value of 0.135. However, ABC achieved this performance about seven hours longer than PSO and about 5 h longer than BWO. Since the parameter optimization of CNN-based models takes much more time than other benchmarks, the convergence speed of algorithms is very important. For this reason, it has been observed that PSO is much more successful than other algorithms with an average run time of 9.438 h. As a result, considering the Jaccard similarity coefficient, it was seen that the model performance increased by 8.1% with the optimization compared to manual parameter selection.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [2180141]en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK). Funds is 1512Entrepreneurship Multi-Phase Programme with project number 2180141.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial bee colony (ABC)en_US
dc.subjectBlack widow optimization (BWO)en_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectGrey wolf optimizer (GWO)en_US
dc.subjectParameter optimizationen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectSegmentationen_US
dc.subjectConvolutional Neural-Networksen_US
dc.subjectClassificationen_US
dc.subjectSelectionen_US
dc.subjectNucleien_US
dc.subjectSearchen_US
dc.subjectImagesen_US
dc.titleOptimization of deep learning based segmentation methoden_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s00500-021-06711-3-
dc.identifier.scopus2-s2.0-85125532173en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authoridINIK, OZKAN/0000-0003-4728-8438-
dc.identifier.volume26en_US
dc.identifier.issue7en_US
dc.identifier.startpage3329en_US
dc.identifier.endpage3344en_US
dc.identifier.wosWOS:000763380000004en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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