An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories

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dc.contributor.authorAksoy, Aziz
dc.date.accessioned2026-06-19T06:37:49Z
dc.date.available2026-06-19T06:37:49Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model's performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells.
dc.identifier.doi10.3390/diagnostics14182093
dc.identifier.issn2075-4418
dc.identifier.issue18
dc.identifier.orcid0000-0002-9683-6691
dc.identifier.pmid39335772
dc.identifier.scopus2-s2.0-85204934746
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics14182093
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5230
dc.identifier.volume14
dc.identifier.wosWOS:001323646400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAksoy, Aziz
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectArtificial Intelligence
dc.subjectClassifiers
dc.subjectDeep Learning
dc.subjectNca
dc.subjectWhite Blood Cell
dc.titleAn Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories
dc.typeArticle

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