Classification of Blood Cells with Convolutional Neural Network Model

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dc.contributor.authorAslan, Emrah
dc.contributor.authorÖzüpak, Yıldırım
dc.date.accessioned2025-02-22T14:17:59Z
dc.date.available2025-02-22T14:17:59Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractWhite Blood Cells are the primary blood cells that come from the bone marrow and are essential for constructing our body's defense system. Leukopenia is a disorder where the body's capacity to fight off infections is compromised due to a low white blood cell count. White blood cell counting is a specialty procedure that is usually carried out by experts and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed Convolutional Neural Network model.en_US
dc.identifier.doi10.17798/bitlisfen.1401294
dc.identifier.endpage326en_US
dc.identifier.issn2147-3129
dc.identifier.issn2147-3188
dc.identifier.issue1en_US
dc.identifier.startpage314en_US
dc.identifier.trdizinid1229661en_US
dc.identifier.urihttps://doi.org/10.17798/bitlisfen.1401294
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1229661
dc.identifier.urihttps://hdl.handle.net/11468/30178
dc.identifier.volume13en_US
dc.indekslendigikaynakTR-Dizin
dc.language.isoenen_US
dc.relation.ispartofBitlis Eren Üniversitesi Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250222
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectWhite Blood Cellen_US
dc.subjectArtificial Intelligent.en_US
dc.titleClassification of Blood Cells with Convolutional Neural Network Modelen_US
dc.typeArticleen_US

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