  Identifying and characterizing the patient's blood samples is indispensable
in diagnostics of malignance suspicious. A painstaking and sometimes subjective
task is used in laboratories to manually classify white blood cells. Neural
mathematical methods as deep learnings can be very useful in the automated
recognition of blood cells. This study uses a particular type of deep learning
i.e., convolutional neural networks (CNNs or ConvNets) for image recognition of
the four (4) blood cell types (neutrophil, eosinophil, lymphocyte and monocyte)
and to enable it to tag them employing a dataset of blood cells with labels for
the corresponding cell types. The elements of the database are the input of our
CNN and they allowed us to create learning models for the image
recognition/classification of the blood cells. We evaluated the recognition
performance and outputs learned by the networks in order to implement a neural
image recognition model capable of distinguishing polynuclear cells (neutrophil
and eosinophil) from those of mononuclear cells (lymphocyte and monocyte). The
validation accuracy is 97.77%.
