Intracranial Brain Hemorrhage Detection Using CNN

Abstract of Detecting Intracranial Hemorrhage with Deep Learning

We using Deep learning algorithms and has been applied for image detection and classification, with good results in the medicine such as medical image analysis.

This paper aims to support the detection of brain hemorrhage in computed tomography (CT) images using deep learning algorithms and convolutional neural networks (CNN).

The motivation of this work is the difficulty of physicians when they face the task to identify brain hemorrhage, but this project easily find out disease especially when they are in the primary stages of brain bleeding, making a misdiagnosis.

A some of  CT studies were used to train and evaluate two convolutional neuronal networks in the task of classifying hemorrhage or non-hemorrhage. The proposed CNN networks reach 92% of accuracy.

Introduction Intracranial hemorrhage detection project

Intracranial hemorrhage (HIC) corresponds to bleeding inside the skull caused by a vascular rupture. Speed of diagnosis is crucial because the mortality reaches up to 62% after 30 days and 34% to 58% of patients die before a month after being diagnosed, and approximately half of these deaths occur within the first 24 hours (Caceres and Goldstein, 2012) (Rodríguez-Yáñez et al., 2013).

This is a reason why HIC is considered a medical emergency and specialists must diagnose it properly and quickly. However, in general medicine settings and emergency rooms, up to 20% of patients with suspected HIC may be misdiagnosed, which is an indicator that bleeding cannot be reliably distinguished without the support of medical imaging techniques (Gross et al. 2019).

Brain neuroimaging computed tomography (CT) for the diagnosis of intracranial hemorrhage, is the most reliable method during the first week after the onset of HIC.

The visualization of intracranial hemorrhage in CT images depends on density, volume, location, relationship with the surrounding structures (Cohen, 1992), all previous properties make HIC diagnosis difficult.

An automatic process for HIC detection in the triage workflow, would significantly decrease the time to diagnosis and expedite treatment.