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

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A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

Yi Chen, Ying Shao, Jie Yan

¹School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023,. China.

CNS & Neurological Disorders Drug Targets

|November 29, 2016

Summary

This study introduces an improved computer vision method for Alzheimer's disease detection, achieving 97.51% accuracy using pseudo Zernike moments and linear regression classification.

Keywords:

Linear regression classifier machine learning pathological brain detection pattern recognition.

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Area of Science:

Medical Imaging
Computer Vision
Neurology

Background:

Alzheimer's disease (AD) diagnosis is increasingly reliant on automated systems.
Previous research utilized pseudo Zernike moments for AD detection (Gorji et al., 2015).
Existing methods explored various classifiers for AD diagnosis.

Purpose of the Study:

To develop an enhanced computer vision approach for accurate Alzheimer's disease detection.
To introduce and evaluate linear regression classification for AD diagnosis.
To improve upon existing state-of-the-art methods for automated AD detection.

Main Methods:

Extraction of 256 features using pseudo Zernike moments (maximum order 15) from single axial brain slices.
Implementation of linear regression classification as the diagnostic classifier.
Comparison with prior methods, including Gorji's approach.

Main Results:

The proposed method achieved a diagnostic accuracy of 97.51%.
Sensitivity was recorded at 96.71% and specificity at 97.73%.
Superior performance compared to Gorji's method and other leading approaches.

Conclusions:

The enhanced method demonstrates superior performance in detecting Alzheimer's disease.
Linear regression classification proves effective for automated AD diagnosis.
This approach offers a promising tool for early and accurate Alzheimer's disease detection.