Intracranial Hemorrhage Detection in Head CT Using Double-Branch Convolutional Neural Network, Support Vector Machine, and Random Forest

Agata Sage,Pawel Badura

doi:10.3390/app10217577

Abstract

Brain hemorrhage is a severe threat to human life, and its timely and correct diagnosis and treatment are of great importance. Multiple types of brain hemorrhage are distinguished depending on the location and character of bleeding. The main division covers five subtypes: subdural, epidural, intraventricular, intraparenchymal, and subarachnoid hemorrhage. This paper presents an approach to detect these intracranial hemorrhage types in computed tomography images of the head. The model trained for each hemorrhage subtype is based on a double-branch convolutional neural network of ResNet-50 architecture. It extracts features from two chromatic representations of the input data: a concatenation of the image normalized in different intensity windows and a stack of three consecutive slices creating a 3D spatial context. The joint feature vector is passed to the classifier to produce the final decision. We tested two tools: the support vector machine and the random forest. The experiments involved 372,556 images from 11,454 CT series of 9997 patients, with each image annotated with labels related to the hemorrhage subtypes. We validated deep networks from both branches of our framework and the model with either of two classifiers under consideration. The obtained results justify the use of a combination of double-source features with the random forest classifier. The system outperforms state-of-the-art methods in terms of F1 score. The highest detection accuracy was obtained in intraventricular (96.7%) and intraparenchymal hemorrhages (93.3%).

Highlights

Intracranial hemorrhage (ICH) relates to bleeding occurring within the intracranial vault.Possible reasons include, i.a., vascular abnormalities, venous infarction, tumor, trauma effects, therapeutic anticoagulation, and cerebral aneurysm [1,2,3,4]
We propose a method for detecting various subtypes of intracranial hemorrhage (SDH, EDH, Intraparenchymal hemorrhage (IPH), IVH, and subarachnoid hemorrhage (SAH)) in the brain computed tomography (CT) scans based on a double-branch Convolutional neural networks (CNNs) for feature extraction and two different classifiers
The best results were achieved for the intraventricular hemorrhage, whereas the worst performance was observed in the case of epidural hemorrhage

Summary

Introduction

Intracranial hemorrhage (ICH) relates to bleeding occurring within the intracranial vault.Possible reasons include, i.a., vascular abnormalities, venous infarction, tumor, trauma effects, therapeutic anticoagulation, and cerebral aneurysm [1,2,3,4]. Intracranial hemorrhage (ICH) relates to bleeding occurring within the intracranial vault. Regardless of the actual cause, a hemorrhage constitutes a major threat. An accurate and rapid diagnosis is crucial for the treatment process and its success. ICH diagnosis relies on patient medical history, physical examination, and non-contrast computed tomography (CT) examination of the brain region. CT examination enables bleeding localization and can indicate the primary causes of ICH [5]. There are several challenges related to the ICH diagnosis and treatment: the urgency of the procedure, a complex and time-consuming decision-making process, an insufficient level of experience in the case of novice radiologists, and the fact that most emergencies occur at nighttime. The accuracy of automated hemorrhage detection should be sufficiently high for medical purposes

Methods

Results

Discussion

Conclusion