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Hemorrhagic Stroke l: Introduction

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A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...

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Updated: Jul 9, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning

Snekhalatha Umapathy^1,2, Murugappan Murugappan^3,4,5, Deepa Bharathi⁶

¹Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai 603203, India.

Diagnostics (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

Early diagnosis of brain bleeds is challenging. This study introduces an ensemble deep learning model combining SE-ResNeXT and LSTM for accurate Intracranial Hemorrhage (ICH) detection, achieving over 99% accuracy.

Keywords:

Grad-CAM model LSTM ResNeXT SE-ResNeXT classification deep learning models intracranial hemorrhage

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Minimally Invasive Endoscopic Intracerebral Hemorrhage Evacuation

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Published on: October 15, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Intracranial Hemorrhage (ICH) diagnosis is critical for patient outcomes but remains challenging.
Early detection of ICH is vital to prevent mortality and morbidity.

Purpose of the Study:

To develop and evaluate an ensemble deep learning model for accurate and early detection of various types of ICH.
To improve the classification accuracy of epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages.

Main Methods:

Utilized an ensemble of Convolutional Neural Networks (CNNs), specifically Squeeze and Excitation-based Residual Networks (SE-ResNeXT) and Long Short-Term Memory (LSTM) networks.
Employed windowing for preprocessing and data augmentation on the RSNA and CQ500 datasets.
Implemented Gradient-weighted Class Activation Mapping (Grad-CAM) for region of interest identification.

Main Results:

The proposed ensemble model achieved an overall accuracy of 99.79% and an F-score of 0.97.
Individual hemorrhage type classification accuracies exceeded 98%, with some reaching 99.89%.
Demonstrated superior performance compared to existing deep learning models in ICH detection.

Conclusions:

The ensemble deep learning approach using SE-ResNeXT and LSTM effectively detects and classifies multiple types of Intracranial Hemorrhage.
The model's high accuracy and AUC scores indicate its potential for clinical application in diagnosing brain bleeds.
This method offers a promising advancement in automated medical image analysis for neurological emergencies.