Elen, AbdullahTuran, M. Kamil2024-09-292024-09-292019https://search.trdizin.gov.tr/tr/yayin/detay/405825https://hdl.handle.net/20.500.14619/11628Blood 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.eninfo:eu-repo/semantics/openAccessArticle152114140582511