Abstract
Aim: This study presents an improved method based on “Gorji et al. Neuroscience. 2015” by introducing a relatively new classifier—linear regression classification.
Method: Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. Results: The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Conclusion: Our method performs better than Gorji’s approach and five other state-of-the-art approaches.Keywords: Alzheimer’s disease, linear regression classification, pseudo Zernike moment.
CNS & Neurological Disorders - Drug Targets
Title:Alzheimer’s Disease Detection by Pseudo Zernike Moment and Linear Regression Classification
Volume: 16 Issue: 1
Author(s): Shui-Hua Wang, Sidan Du, Yin Zhang, Preetha Phillips, Le-Nan Wu, Xian-Qing Chen and Yu-Dong Zhang
Affiliation:
Keywords: Alzheimer’s disease, linear regression classification, pseudo Zernike moment.
Abstract: Aim: This study presents an improved method based on “Gorji et al. Neuroscience. 2015” by introducing a relatively new classifier—linear regression classification.
Method: Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. Results: The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Conclusion: Our method performs better than Gorji’s approach and five other state-of-the-art approaches.Export Options
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Cite this article as:
Wang Shui-Hua, Du Sidan, Zhang Yin, Phillips Preetha, Wu Le-Nan, Chen Xian-Qing and Zhang Yu-Dong, Alzheimer’s Disease Detection by Pseudo Zernike Moment and Linear Regression Classification, CNS & Neurological Disorders - Drug Targets 2017; 16 (1) . https://dx.doi.org/10.2174/1871527315666161111123024
DOI https://dx.doi.org/10.2174/1871527315666161111123024 |
Print ISSN 1871-5273 |
Publisher Name Bentham Science Publisher |
Online ISSN 1996-3181 |
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