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

TL;DR

This study demonstrates that deep convolutional neural networks combined with transfer learning can accurately diagnose hydrocephalus from brain MRI images, achieving high sensitivity and specificity even with limited data.

Contribution

The paper introduces a novel application of transfer learning with deep CNNs for hydrocephalus detection in MRI images, showing high accuracy with limited training data.

Findings

Accuracy of 97% in hydrocephalus detection

Sensitivity of 98% for identifying hydrocephalus

Specificity of 96% indicating reliable classification

Abstract

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.