  The hydrocephalus can be either an independent disease or a concomitant
symptom of a number of pathologies, therefore representing an urgent issue in
the present-day clinical practice. Deep Learning is an evolving technology and
the part of a broader field of Machine Learning. Deep learning is currently
actively researched in the field of radiology. The aim of this study was to
evaluate deep learning applicability to the diagnostics of hydrocephalus with
the use of MRI images. We retrospectively collected, annotated, and
preprocessed the brain MRI data of 200 patients with and without radiological
signs of hydrocephalus. We applied a state-of-the-art deep convolutional neural
network in conjunction with transfer learning method to train a hydrocephalus
classifier model. Using deep convolutional neural networks, we achieved a high
quality of machine learning model. Accuracy, sensitivity, and specificity of
hydrocephalus signs identification was 97%, 98%, and 96% respectively. In this
study, we demonstrated the capacity of deep neural networks to identify
hydrocephalus syndrome using brain MRI images. Applying transfer learning
technique, the high quality of classification was achieved although trained on
rather limited data.
