Intelligent system to detect cerebral auto - regulation and brain damages after traumatic head injury

Abstract

Cerebral auto-regulation is examined only by considering the clinical signs like signs of base of skull fracture, Poor attention, Loss of consciousness, bleeding from an ear, examine the eyes, Re-examine the pupils, Examine eye movements. A change in these clinical signs will result in timely investigation and treatment of emergent problems and will help prevent secondary brain injury. It is only through clinical symptoms, that the doctors reveal that there can be secondary brain injuries or not. The severity of brain damage can vary with the type of brain injury. There can be damage to a small area or damage to several areas of the brain. Yet it doesn’t solve the problem of detecting the cerebral auto-regulation and predicts the severity of the brain damage. The main aim of this project is to detect the failing point of the auto-regulation and identify the brain damage areas and predict the severity of the damage. A literature review and an online survey of knowledge recourses were conducted to gather the requirements to design a solution. There are two main ways of detecting the brain damages. Firstly, users can upload a CT damaged brain image and segment the damage areas using the SVM method. Secondly, the damage areas can be segmented by using thresholding process. This process will bring the vision of predicting the severity of the brain damage.