# Classification of glomerular hypercellularity using convolutional features and support vector machine

## TL;DR

This paper presents a novel deep learning approach combining CNN features and SVM for accurate classification of glomerular hypercellularity in kidney histology images, outperforming previous methods and enabling detailed lesion sub-classification.

## Contribution

Introduces a new CNN-SVM architecture for hypercellularity detection, achieving near-perfect results and first application of deep learning to this specific kidney lesion dataset.

## Key findings

- Achieved near 100% accuracy in binary classification
- Outperformed state-of-the-art methods on the same dataset
- Successfully classified hypercellularity sub-lesions with only 4% failure rate

## Abstract

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.