Shui-Hua Wang , Sidan Du , Yin Zhang , Preetha Phillips , Le-Nan Wu , Xian-Qing Chen , Yu-Dong Zhang 1 Show Affiliations »
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Entities: Disease
Keywords:
Alzheimer’s disease; linear regression classification; pseudo Zernike moment.
Mesh： See more »
Year: 2017
PMID： 27834130 DOI： 10.2174/1871527315666161111123024
Source DB: PubMed Journal: CNS Neurol Disord Drug Targets ISSN： 1871-5273 Impact factor: 4.388