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A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet.

Oznur Ozaltin¹, Orhan Coskun², Ozgur Yeniay¹

¹Institute of Science, Department of Statistics, Beytepe Campus, Hacettepe University, Ankara 06800, Turkey.

Bioengineering (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces a hybrid artificial intelligence (AI) approach for rapid brain stroke detection using computed tomography (CT) scans. The OzNet-mRMR-NB model achieved 98.42% accuracy, significantly improving early diagnosis and treatment.

Keywords:

OzNet brain stroke classification computed tomography convolution neural networks feature extraction mRMR

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Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Brain stroke is a critical condition requiring prompt diagnosis for effective treatment.
Computed tomography (CT) scans are vital for rapid stroke diagnosis, but manual analysis can be time-consuming and prone to errors.
Efficient AI algorithms are needed to expedite stroke detection from CT images.

Purpose of the Study:

To design and evaluate hybrid AI algorithms for accurate and efficient binary classification of brain stroke in CT images.
To develop a novel Convolutional Neural Network (CNN) architecture, OzNet, for feature extraction in stroke detection.
To integrate OzNet with machine learning classifiers and feature selection methods for enhanced diagnostic performance.

Main Methods:

A new CNN architecture, OzNet, was developed for analyzing brain CT images.
Hybrid algorithms were created by combining OzNet with feature selection methods like minimum Redundancy Maximum Relevance (mRMR).
OzNet's extracted features were reduced from 4096 to 250 using mRMR, followed by classification using algorithms such as Naïve Bayes (NB), Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM).

Main Results:

The OzNet-mRMR-NB hybrid algorithm demonstrated superior performance in detecting stroke from brain CT images.
This hybrid model achieved a high accuracy of 98.42% and an Area Under the Curve (AUC) of 0.99.
The study successfully reduced feature dimensions while maintaining high classification accuracy.

Conclusions:

The proposed OzNet-mRMR-NB hybrid algorithm is highly effective for automated stroke detection in brain CT scans.
This AI-driven approach offers a promising solution for faster and more accurate stroke diagnosis, potentially reducing treatment delays.
The findings highlight the potential of combining deep learning with traditional machine learning for medical image analysis.