Alzheimer’s disease detection by pseudo zernike moment and linear regression classification

Shui Hua Wang, Sidan Du, Yin Zhang, Preetha Phillips, Le Nan Wu, Xian Qing Chen, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)11-15
Number of pages5
JournalCNS and Neurological Disorders - Drug Targets
Issue number1
Publication statusPublished - 1 Feb 2017
Externally publishedYes


  • Alzheimer’s disease
  • Linear regression classification
  • Pseudo Zernike moment


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