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The hydrocephalus can be either an independent disease or a concomitantsymptom of a number of pathologies, therefore representing an urgent issue inthe present-day clinical practice. Deep Learning is an evolving technology andthe part of a broader field of Machine Learning. Deep learning is currentlyactively researched in the field of radiology. The aim of this study was toevaluate deep learning applicability to the diagnostics of hydrocephalus withthe use of MRI images. We retrospectively collected, annotated, andpreprocessed the brain MRI data of 200 patients with and without radiologicalsigns of hydrocephalus. We applied a state-of-the-art deep convolutional neuralnetwork in conjunction with transfer learning method to train a hydrocephalusclassifier model. Using deep convolutional neural networks, we achieved a highquality of machine learning model. Accuracy, sensitivity, and specificity ofhydrocephalus signs identification was 97%, 98%, and 96% respectively. In thisstudy, we demonstrated the capacity of deep neural networks to identifyhydrocephalus syndrome using brain MRI images. Applying transfer learningtechnique, the high quality of classification was achieved although trained onrather limited data.

Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique