Classification of analyzable metaphase images using transfer learning and fine tuning

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dc.contributor.authorAlbayrak, Abdulkadir
dc.date.accessioned2021-12-21T08:56:06Z
dc.date.available2021-12-21T08:56:06Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionWOS:000722473100001
dc.descriptionPMID: 34822119
dc.description.abstractChromosomes are bodies that contain human genetic information. Chromosomal disorders can cause structural and functional disorders in individuals. Detecting the metaphase stages of the cells accurately is a crucial step to detect possible defects in chromosomes. Thus, it is vital at this stage to identify the identical chromosome of each chromosome, to perform the pairing process, and to identify problems arising from this process. In this study, it was investigated whether the analyzable metaphase images can be analyzed by using the transfer learning and fine tuning approaches of deep learning models. The weights of VGG16 and InceptionV3 models trained with ImageNet data set were transferred to this problem and the classification process was carried out. True positive ratio values are 99%(+/- 0.9) and 99%(+/- 0.9) for VGG and Inception networks, respectively. The classification performances obtained depending on the changing training set ratios are presented comparatively in figures. F-measure, precision, and recall values obtained for the VGG and Inception networks were observed as 99%(+/- 1.0) and 99%(+/- 1.0), respectively. F-measure, precision, and recall values of VGG and Inceptionv3 networks are also presented with respect to the ratio of training size. The obtained results have compared with the state-of-the-art methods in the literature and supported with the tables and graphics. The training phase was also accelerated by using transfer learning and fine tuning methods. Transfer learning and fine tuning processes have almost similar performance as the models used in the literature and trained from scratch in metaphase Graphical Abstract The Flowchart of the proposed system for classifying metaphase candidates detection.en_US
dc.identifier.citationAlbayrak, A. (2021). Classification of analyzable metaphase images using transfer learning and fine tuning. Medical & Biological Engineering & Computing, 60(1), 239-248.en_US
dc.identifier.doi10.1007/s11517-021-02474-z
dc.identifier.endpage248
dc.identifier.issue1
dc.identifier.pmid34822119
dc.identifier.scopus2-s2.0-85119877984
dc.identifier.scopusqualityN/A
dc.identifier.startpage239
dc.identifier.urihttps://hdl.handle.net/11468/8595
dc.identifier.volume60en_US
dc.identifier.wosWOS:000722473100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAlbayrak, Abdulkadir
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMedical & Biological Engineering & Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectKaryotypingen_US
dc.subjectMetaphase detectionen_US
dc.subjectTransfer learningen_US
dc.subjectVgg16en_US
dc.subjectInceptionv3en_US
dc.subjectDeep learningen_US
dc.titleClassification of analyzable metaphase images using transfer learning and fine tuningen_US
dc.titleClassification of analyzable metaphase images using transfer learning and fine tuning
dc.typeArticleen_US

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