Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining
International Journal of Biomedical Science and Engineering
Volume 4, Issue 6, December 2016, Pages: 50-54
Received: Oct. 31, 2016; Accepted: Dec. 26, 2016; Published: Feb. 15, 2017
Views 88      Downloads 5
Abdalla R. Gad, Electronic and Communication Department, Faculty of Engineering, October High Institute for Engineering and Technology, Giza, Egypt
N. M. Hussein Hassan, Electronic and Communication Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
Rania A. Abul Seoud, Electronic and Communication Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
Tamer M. Nassef, Computer and Software Department, Faculty of Engineering, Misr University for Science and Technology, Giza, Egypt
Article Tools
Follow on us
The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.
Alzheimer's Disease, Magnetic Resonance Imaging, Feature Extraction, Classification, Support Vector Machine, K-nearest Neighbor
To cite this article
Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef, Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining, International Journal of Biomedical Science and Engineering. Vol. 4, No. 6, 2016, pp. 50-54. doi: 10.11648/j.ijbse.20160406.11
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
R. Brookmeyer, S. Gray, C. Kawas, “Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset”, American Journal of Public Health, vol. 88, no. 9, pp. 1337–1342, 1998.
LM. Bloudek, ED. Spackman, M. Blankenburg, SD. Sullivan, “Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer's disease”, Journal of Alzheimers Disease, vol. 26, no. 4 pp. 627-645, 2011.
Y. M. Alkhiary, T. M. Nassef, I. A. Yassine, and S. B. Tayel, “A new numerical model to analyze stress distribution of TMJ disc from 2-D MRI scans,” Advances in Computing, vol. 2, no. 5, pp. 66-75, 2012.
L. Fratiglioni, A. Ahlbom, M. Viitanen, and B. Winblad, “Risk factors for late-onset Alzheimer's disease: a population-based, case–control study” Annals of neurology, vol. 33, no. 3, pp. 258-266, 1993.
D. ping Tian, “A review on image feature extraction and representation techniques”, International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.
Y. Fan, SM. Resnick, X. Wu, and C Davatzikos, “Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study”, NeuroImage, vol. 41, no. 2, pp. 277–285, 2008.
Y. Wenlu, H. Fangyu, C. Xinyun and H. Xudong, “ICA-based automatic classification of PET images from ADNI database”, International Conference on Neural Information Processing, pp. 265-272, November 2011.
M. Yang, K. Kpalma and J. Ronsin, “A survey of shape feature extraction techniques”, Pattern Recognition, pp. 43-90, 2008.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, Computer Vision and Pattern Recognition, IEEE Computer Society Conference, vol. 1, pp. 886–893, June 2005.
S. Arivazhagan and L. Ganesan, “Texture classification using wavelet transform”, Pattern Recognition Letters, vol. 24, no. 9, pp. 1513-1521, 2003.
J. Zhang, C. Yu, G. Jiang, W. Liu and L. Tong, “3D texture analysis on MRI images of Alzheimer's disease” Brain imaging and behavior, vol. 6, no. 1, pp. 61-69, 2012.
A. Savio, M. García-Sebastián, C. Hernández, M. Graña, and J. Villanúa, “Classification results of artificial neural networks for Alzheimer's disease detection” Springer Berlin Heidelberg, Intelligent Data Engineering and Automated Learning-IDEAL, pp. 641-648, 2009.
B. Magnin, L. Mesrob, L. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehéricy, and H. Benali, “Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI “Neuroradiology, vol. 51, no. 2, pp. 73–78, 2009.
B. Wang, Y. Zeng and Y. Yang, “Generalized nearest neighbor rule for pattern classification", 7th World Congress on Intelligent Control and Automation, pp. 8465-8470, 2008.
A. Payan and G. Montana, “Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks” arXiv preprint arXiv, February 2015.
Y. Zhang, S. Wang, and Z. Dong, “Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree”
Progress in Electromagnetics Research, vol. 144, pp. 171-184, 2014.
C. Cabral, and M. Silveira, “Classification of Alzheimer's disease from FDG-PET images using favourite class ensembles”, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) pp. 2477-2480, 2013.
NH. Rajini, and R. Bhavani, “Classification of MRI brain images using k-nearest neighbor and artificial neural network”, In Recent Trends in Information Technology, International Conference on IEEE, pp. 563-568, 2011.
VP. Rathi and S. Palani, “Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis”, arXiv preprint arXiv, 2012.
M. Silveira, and J. Marques, “Initiative Boosting Alzheimer disease diagnosis using PET images”, 20th IEEE International Conference In Pattern Recognition (ICPR), pp. 2556-2559, 2010.
G. Chen, BD. Ward, C. Xie, W. Li, Z. Wu, JL. Jones, M. Franczak, P. Antuono, and SJ. Li. “Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging”, Radiology, vol. 259, no. 1, pp. 213-221, 2011.
C. Aguilar, E. Westman, JS. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, S. Lovestone, C. Spenger and, A. Simmons, “Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment “Psychiatry Research: Neuroimaging,; vol. 212, no. 2, pp. 89-98, 2013.
C. Cortes, V. Vapnik, “Support-Vector Networks” Machine Learning vol. 20, no. 3, pp. 273–297, 1995.
CJ. Burges. “A tutorial on support vector machines for pattern recognition”, Data mining and knowledge discovery, vol. 2, no. 2, pp121-67, 1998.
T. Cover and P. Hart “Nearest neighbor pattern classification” IEEE transactions on information theory, vol. 13, no. 1, pp. 7-21, 1976.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931