  Glomeruli are histological structures of the kidney cortex formed by
interwoven blood capillaries, and are responsible for blood filtration.
Glomerular lesions impair kidney filtration capability, leading to protein loss
and metabolic waste retention. An example of lesion is the glomerular
hypercellularity, which is characterized by an increase in the number of cell
nuclei in different areas of the glomeruli. Glomerular hypercellularity is a
frequent lesion present in different kidney diseases. Automatic detection of
glomerular hypercellularity would accelerate the screening of scanned
histological slides for the lesion, enhancing clinical diagnosis. Having this
in mind, we propose a new approach for classification of hypercellularity in
human kidney images. Our proposed method introduces a novel architecture of a
convolutional neural network (CNN) along with a support vector machine,
achieving near perfect average results with the FIOCRUZ data set in a binary
classification (lesion or normal). Our deep-based classifier outperformed the
state-of-the-art results on the same data set. Additionally, classification of
hypercellularity sub-lesions was also performed, considering mesangial,
endocapilar and both lesions; in this multi-classification task, our proposed
method just failed in 4\% of the cases. To the best of our knowledge, this is
the first study on deep learning over a data set of glomerular hypercellularity
images of human kidney.
