Classifying White Blood Cells Using Machine Learning Algorithms

Authors : Abdullah ELEN, M Kamil TURAN

Pages : 141-152

Doi:10.29137/umagd.498372

View : 23 | Download : 14

Publication Date : 2019-01-31

Article Type : Research Paper

Abstract :Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. 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 classification of white blood cells and their performances were compared. 35 different geometric and statistical insert ignore into journalissuearticles values(texture); features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression insert ignore into journalissuearticles values(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 of diseases for hematologists and internal medicine specialists.
Keywords : WBC classification, leukocytes, blood cells, machine learning