An efficient multi-objective memetic genetic algorithm for medical image handling and health safety to support systems in medical internet of things

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dc.contributor.authorSafi, Hayder H.
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T11:34:07Z
dc.date.available2021-05-15T11:34:07Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractOver the most recent couple of decades, the Evolutionary Algorithms (EA) have been considered as a typical dynamic research zone in the field of medicinal picture preparing and wellbeing administrations. This is a direct result of the presence of numerous streamlining issues in this field which can be comprehended utilizing developmental calculations. Besides, numerous certifiable streamlining issues in the restorative picture handling and wellbeing administrations have more than one target work, and for the most part the issue goals are in struggle with one another. The traditional multi-objective transformative calculations perform well when the streamlining issue has less than three goal functions, where the execution of these calculations altogether degrades when the improvement issue has high number of destinations. To deal with this issue, there is a requirement for growing new versatile transformative streamlining mechanisms which can handle the high target capacities in the medicinal picture preparing and wellbeing administrations advancement issues. In this paper, we propose a new developmental calculation dependent on the NSGA-II calculation to productively take care of the many-target enhancement issues. The proposed calculation adds three plans to improve the capacity of NSGA-II calculation when managing with high objective dimensional streamlining issues. Another productive arranging strategy, savvy file and straightforward nearby pursuit are utilized to accelerate the solutions combination procedure to the POF and upgrade the decent variety of the arrangements. The proposed calculation is contrasted and the cutting edge multi-target advancement calculations utilizing five DTLZ test issues. The outcomes demonstrate that our proposed calculation essentially beats alternate calculations when the quantity of target capacities is high. Moreover, we applied our proposed algorithm on medical imaging health problem which is the melanoma recognition problem to enhance the early detection of melanoma disease.en_US
dc.identifier.doi10.1166/jmihi.2020.2833
dc.identifier.endpage203en_US
dc.identifier.issn2156-7018
dc.identifier.issn2156-7026
dc.identifier.issue1en_US
dc.identifier.startpage194en_US
dc.identifier.urihttps://doi.org/10.1166/jmihi.2020.2833
dc.identifier.urihttps://hdl.handle.net/20.500.12939/282
dc.identifier.volume10en_US
dc.identifier.wosWOS:000496925300032
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthorBayat, Oğuz
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorSafi, Hayder H.
dc.language.isoen
dc.publisherAmer Scientific Publishersen_US
dc.relation.ispartofJournal of Medical Imaging and Health Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMany-Objective Optimization Problemsen_US
dc.subjectNSGA-IIen_US
dc.subjectMulti-Objective Problemsen_US
dc.subjectHealth Safety Servicesen_US
dc.subjectMedical Image Handlingen_US
dc.subjectEvolutionary Algorithmsen_US
dc.titleAn efficient multi-objective memetic genetic algorithm for medical image handling and health safety to support systems in medical internet of things
dc.typeArticle

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