Intracranial hemorrhage detection and classification using deep learning

Document Type

Book Chapter

Publication Title

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Abstract

This chapter deals with the detection and classification of intracranial hemorrhage (ICH), which is defined as bleeding inside the skull. ICH may lead to a hemorrhagic stroke, and while this is a less common type of stroke, it still accounts for 10%-15% of cases. According to the Centers for Disease Control and Prevention, stroke accounted for one in every six people dying from cardiovascular diseases in 2018 and this makes ICH a very serious medical condition. It requires urgent, often surgical treatment, and the chances of the patient’s survival depend heavily on the speed of diagnosis. Traditionally, trained human experts locate and identify the type of ICH by inspecting radiological images, such as computerized tomography (CT) scan or magnetic resonance imaging scan images of the patient’s skull. Although this is a standard procedure in cases of head trauma, or for a patient experiencing acute neurological symptoms, the process is complex and slow. Efforts have been made to automate this process using machine learning algorithms to aid in diagnosis, with varying success. Here we describe the different types of ICH and review the different techniques that have been used to address the problem of detecting and classifying them. We also discuss some of the characteristics of this problem that make it especially challenging. Finally, we describe several approaches that we tried on a rich CT image dataset with the results obtained.

First Page

1

Last Page

14

DOI

10.1016/B978-0-323-90037-9.00009-6

Publication Date

1-1-2022

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