Hydrocephalus classification in brain computed tomography medical images using deep learning

Abstract

Recent technological advancements, like big data analytics, is driving the growing adoption of cyber-physical systems and digital twins in the area of healthcare. Congenital hydrocephalus is one important example of recent healthcare data analytics. Congenital hydrocephalus is a buildup of excess cerebrospinal fluid (CSF) in the brain at birth. Congenital hydrocephalus can be lethal without treatment and represents an urgent issue in present-day clinical practice. Congenital hydrocephalus has a significant effect on a human entire life since it causes damage to the brain. It is important to accurately diagnose hydrocephalus early, which will help in the early treatment of the infant by a surgical procedure called ventriculoperitoneal (VP) shunt which will reduce the damage caused by hydrocephalus on the brain. Deep Learning is an evolving technology that is currently actively researched in the field of radiology. Compared to the traditional hydrocephalus diagnosing techniques, automatic diagnosing algorithms in deep learning can save diagnosis time, improve diagnosing accuracy, reduce cost, and reduce the radiologist's workload. In this paper, we have used a novel dataset collected from king Hussein medical center hospital in Jordan that consists of CT scans for hydrocephalus and non-hydrocephalus infants, the dataset has gone through multiple stages in preprocessing which are; cropping and filtering, normalization, segmentation (three segmentation techniques have been applied), and augmentation. These data have been used to build deep learning and machine learning models that will help physicians in the early and accurate diagnosing of congenital hydrocephalus which will lead to a decrease in the death rate and brain damage. The results of our models were impressive with a 98.5% accuracy for congenital hydrocephalus classification in infants' brain CT images.