Classifying white blood cells using machine learning algorithms

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dc.contributor.authorElen, Abdullah
dc.contributor.authorTuran, M. Kamil
dc.date.accessioned2024-09-29T16:32:26Z
dc.date.available2024-09-29T16:32:26Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBlood and its components have an important place in human life and are the best indicator tool in determining many pathologicalconditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases.In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classificationof white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have beenextracted from blood images for training and test parameters of machine learning algorithms. According to the results, theMultinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success.The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis ofdiseases for hematologists and internal medicine specialists.en_US
dc.identifier.endpage152en_US
dc.identifier.issue1en_US
dc.identifier.startpage141en_US
dc.identifier.trdizinid405825en_US
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/405825
dc.identifier.urihttps://hdl.handle.net/20.500.14619/11628
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleClassifying white blood cells using machine learning algorithmsen_US
dc.typeArticleen_US

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