Automated Hematocyte Classification using Hybrid Deep Learning Model

Numerous applications of deep learning have already shown their efficacy, and more and more people are beginning to accept that deep learning is better than older machine learning methods. Deep learning algorithms, in particular, have enormous benefits in the medical profession, where a huge quantity of pictures must be analysed and assessed. The categorisation of blood cells, one of the most difficult problems in blood diagnostics, intends to create a deep-learning model. The nucleus of cells provides important data to specialists for disease classification, as the massive diseases can be differentiated by observing the classification of hematocytes. This addresses two primary goals: giving a stained image of a WBC and classifying it as either polynuclear or mononuclear. Second, using a stained picture, identify the eosinophils, neutrophils, lymphocytes, and monocytes to determine the kind of hematocytes. The classification of hematocytes is resource-intensive; hence, this work, intend to present a novel framework to improve the detection and classification of hematocytes.