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Related Experiment Video

Updated: Oct 19, 2025

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Published on: November 20, 2015

Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine.

Siyuan Lu¹, Shuaiqi Liu², Shui-Hua Wang³

¹School of Informatics, University of Leicester, Leicester, United Kingdom.

Frontiers in Computational Neuroscience

|September 27, 2021

Summary

This summary is machine-generated.

This study introduces an advanced method for detecting cerebral microbleeds (CMBs) in brain MRIs. Combining a convolutional neural network (CNN) with an extreme learning machine (ELM) improves diagnostic accuracy for these critical indicators of neurological disease.

Keywords:

bat algorithm computer-aided diagnosis convolutional neural network deep learning extreme learning machine

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Last Updated: Oct 19, 2025

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

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Published on: November 20, 2015

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

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

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

Published on: April 13, 2013

Area of Science:

Neuroimaging
Artificial Intelligence in Medicine
Neurology

Background:

Cerebral microbleeds (CMBs) are small brain lesions linked to stroke, dementia, and mortality.
Early diagnosis of CMBs is crucial for timely intervention and patient management.

Purpose of the Study:

To develop and evaluate a novel approach for detecting CMBs in brain magnetic resonance images (MRIs).
To enhance the accuracy and efficiency of CMB detection using deep learning techniques.

Main Methods:

A 13-layer convolutional neural network (CNN) was designed for CMB detection.
An extreme learning machine (ELM) was integrated to replace the final layers of the CNN.
The bat algorithm was employed to optimize ELM parameters.
Hold-out validation and averaging of five runs were used for performance evaluation.

Main Results:

Replacing the last five layers of the CNN with ELM yielded optimal detection results.
The proposed hybrid CNN-ELM method demonstrated high accuracy in CMB detection.
Performance was validated against state-of-the-art algorithms, showing competitive results.

Conclusions:

The developed hybrid CNN-ELM approach offers an accurate and effective method for CMB detection in brain MRIs.
This technique holds promise for improving the early diagnosis of conditions associated with cerebral microbleeds.
Further research can explore broader clinical applications of this AI-driven diagnostic tool.