Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.
W pracy przedstawiono analizę ilościową wybranych parametrów morfologicznych krwi po jej czasowym kontakcie z materiałem węglowym LTI. Na podstawie uzyskanych wyników nie stwierdzono zmian w wartościach parametrów układu czerwonokrwinkowego, natomiast zaobserwowano zmiany w wartościach parametrów układu białokrwinkowego i płytkowego. Zaobserwowane zmiany w wartościach parametrów morfologicznych nie przekraczają zakresu wartości referencyjnych dla tych wskaźników.
EN
A quantitative analysis of selected morphological parameters of blood after its temporal contact with LTI carbon material was presented in the study. The obtained results indicate that the values of parameters of erythrocyte system did not change, while the values of parameters of leukocyte and platelet systems were altered. The changes in the values of parameters did not exceed the range of referential values for these indexes.