Alzheimer’s Disease Detection by Pseudo Zernike Moment and Linear Regression Classification

Author(s): Shui-Hua Wang, Sidan Du, Yin Zhang, Preetha Phillips, Le-Nan Wu, Xian-Qing Chen, Yu-Dong Zhang.

Journal Name: CNS & Neurological Disorders - Drug Targets
(Formerly Current Drug Targets - CNS & Neurological Disorders)

Volume 16 , Issue 1 , 2017

Become EABM
Become Reviewer

Graphical Abstract:


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.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 16
ISSUE: 1
Year: 2017
Page: [11 - 15]
Pages: 5
DOI: 10.2174/1871527315666161111123024
Price: $58

Article Metrics

PDF: 28
HTML: 2