In this work, the authors classify leukocyte images using the neural network architectures that won the
annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and
the same networks trained from scratch in order to select the ones that achieve the best performance for
the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The
analysis of the results takes into account the amount of training required, the regularization techniques
used, the training time, and the accuracy in image classification. The best classification results, on the
order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like
the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.
