Texture Analysis Versus Deep Learning in MRI-based Classification of Renal Failure

In this paper, we compare two approaches to automatically classify MR images of kidney lesions. The first approach involves the extraction of texture features for manually delineated kidney regions of interest (ROI) and then the classification of feature vectors with a model trained in a supervised manner. This classic machine learning approach is then challenged by a convolutional neural network-based method, which performs image classification in the learned latent feature space. In both cases, We aim to verify the hypothesis that it is possible to differentiate the state of renal failure between three classes: control, active inflammation, and chronic malformations based on the information content of the T1-weighted non-contrast enhanced MRI. The experiments performed on a sample of 25 showed superior performance of the convolutional neural network, for which we obtained the accuracy score at the level of 94% against 80% for the texture-based classification.