A Deep Learning Method Of White Blood Cell Identification In Peripheral Blood

Abstract

By monitoring leukocyte ratios, computerized leukocyte detection and classification aids in the diagnosis of numerous blood-related disorders. Different methods developed by various researchers use conventional learning to categories multiple kinds of leukocytes.
Deep learning, as opposed to traditional learning, which doesn't retain any knowledge that may be carried over from one modeling to the next, is used for categorization and segmentation in our proposed method. Standard learning doesn't have any of these capabilities. The pipeline of the recommended algorithm consists of two stages: the first is semantic separation, and the second is basis for evaluating on domain adaptation. We used DeepLabv3+ for leukocyte division and Alex Net to classify five different types of
leukocytes found in peripheral circulation from the whole blood smears microscopy photos by making use of information obtained from previously completed jobs. In order to conduct the experiment, a data set consisting of 245 cells was taken from a series of microscopy pictures. These cells represented five distinct types of leukocytes. When compared to other methods, the suggested methodology achieved a classification performance of 98.90% and a mean precision of 97.37% (with an IoU value of 0.7) when locating white blood cells. This was achieved in locating the cells.