A Zernike moment based method for classification of Alzheimer's disease from structural MRI

This paper proposed a method based on Zernike moments to classify the various stages of Alzheimer's Disease(AD) from structural MRIs. The proposed method is benefited from all three orthogonal directions of MRIs i.e. Axial, Sagittal and Coronal images. Three back-propagation algorithms had been...

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Bibliographic Details
Published in:	2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) pp. 38 - 43
Main Authors:	Shams-Baboli, Aref, Ezoji, Mehdi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2017
Subjects:	Alzheimer's disease Artificial neural network Artificial neural networks Classification algorithms Dementia Feature extraction magnetic resonance images Magnetic resonance imaging mild cognitive impairment Training Zernike moments
Online Access:	Get full text
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Summary:	This paper proposed a method based on Zernike moments to classify the various stages of Alzheimer's Disease(AD) from structural MRIs. The proposed method is benefited from all three orthogonal directions of MRIs i.e. Axial, Sagittal and Coronal images. Three back-propagation algorithms had been used to train the neural network with seven neurons in hidden layer to reach the best accuracy. We experimented this method with 232 MRIs from OASIS database. 70 percent of the subjects had been used for training and the other 30 percent was used to evaluate the trained network. We achieved accuracy of 86.46 percent in multiclass mode and 96.67 percent of accuracy in two class mode between HC and AD.
DOI:	10.1109/PRIA.2017.7983061