Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

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.

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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).