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PodNet: Ensemble-based Classification of Podocytopathy on Kidney Glomerular Images

Podocyte lesions in renal glomeruli are identified by pathologists using visual analyses of kidney tissue sections (histological images). By applying automatic visual diagnosis systems, one may reduce the subjectivity of analyses, accelerate the diagnosis process, and improve medical decision accuracy. Towards this direction, we present here a new data set of renal glomeruli histological images for podocitopathy classification and a deep neural network model. The data set consists of 835 digital images (374 with podocytopathy and 430 without podocytopathy), annotated by a group of pathologists. Our proposed method (called here PodNet) is a classification method based on deep neural networks (pre-trained VGG19) used as features extractor from images in different color spaces. We compared PodNet with other six state-of-the-art models in two data set versions (RGB and gray level) and two different training contexts: pre-trained models (transfer learning from Imagenet) and from-scratch, both with hyperparameters tuning. The proposed method achieved classification results to 90.9% of f1-score, 88.9% precision, and 93.2% of recall in the final validation sets.