Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique
This addresses the clinical need for automated hydrocephalus detection in radiology, but it is incremental as it uses existing methods on a new medical dataset.
The study tackled the problem of diagnosing hydrocephalus from brain MRI images by applying a deep convolutional neural network with transfer learning, achieving 97% accuracy, 98% sensitivity, and 96% specificity.
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.