Alzheimer's disease detection using a Projection Based Learning Meta-cognitive RBF Network

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Sateesh Babu, G ^a   Suresh, S ^a   Mahanand, B S ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b SRI JAYACHAMARAJENDRA COLLEGE OF ENGINEERING   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER'S DISEASE; CLASS LABELS; COMPUTATIONAL EFFORT; DATA SETS; DECISION SURFACES; ENERGY FUNCTIONS; HIDDEN NEURONS; METACOGNITIVES; OPEN ACCESS; PERFORMANCE EVALUATION; SIMULATION STUDIES; VOXEL-BASED MORPHOMETRY;

ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS; NEURONS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84865088172     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252419     Document Type: Conference Paper

References (25)

1. P. M. Thompson, K. M. Hayashi, R. A. Dutton, M.-C. Chiang, A. D. Leow, E. R. Sowell, G. De Zubicaray, J. T. Becker, O. L. Lopex, H. J. Aizenstein, and A. W. Toga, "Tracking Alzheimer's Disease," Annals of the New York Academy of Sciences, vol. 1097, no. 1, pp. 183-214, 2007.
2. S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, "Automatic classification of MR scans in Alzheimer's disease," Brain, vol. 131, no. 3, pp. 681-689, 2008.
3. C. R. Jack, R. C. Petersen, P. C. O'Brien, and E. G. Tangalos, "MRbased hippocampal volumetry in the diagnosis of Alzheimer's disease." Neurology, vol. 42, no. 1, pp. 183-188, 1992.
4. B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini- Issac, O. olliot, 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. 73-83, 2009.
5. J. Ashburner and K. J. Friston, "Voxel-Based Morphometry-The Methods," NeuroImage, vol. 11, no. 6, pp. 805-821, 2000.
6. M. J. McKeown and T. J. Sejnowski, "Independent component analysis of fMRI data: examining the assumptions." Human Brain Mapping, vol. 6, no. 5-6, pp. 368-372, 1998.
7. L. Xu, G. Pearlson, and V. D. Calhoun, "Joint source based morphometry identifies linked gray and white matter group differences." NeuroImage, vol. 44, no. 3, pp. 777-789, 2009.
8. C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S. M. Resnick, "Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging." Neurobiology of Aging, vol. 29, no. 4, pp. 514-523, 2008.
9. R. Isaacson and F. Fujita, "Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning," Journal of the Scholarship of Teaching and Learning, vol. 6, no. 1, pp. 39-55, 2006.
10. B. S. Mahanand, S. Suresh, N. Sundararajan, and M. A. Kumar, "Alzheimer's disease detection using a Self-adaptive Resource Allocation Network classifier," in The International Joint Conference on Neural Networks (IJCNN), 2011, pp. 1930-1934.
11. B. Mahanand, S. Suresh, N. Sundararajan, and M. A. Kumar, "Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network," Neural Networks, 2012, doi:10.1016/j.neunet.2012.02.035.
12. S. Suresh, K. Dong, and H. J. Kim, "A sequential learning algorithm for self-adaptive resource allocation network classifier," Neurocomputing, vol. 73, no. 16-18, pp. 3012-3019, 2010.
13. S. Suresh, R. Savitha, and N. Sundararajan, "A Sequential Learning Algorithm for Complex-valued Self-regulating Resource Allocation Network-CSRAN," IEEE Transactions on Neural Networks, vol. 22, no. 7, pp. 1061-1072, 2011.
14. G. Sateesh Babu and S. Suresh, "Meta-cognitive Neural Network for classification problems in a sequential learning framework," Neurocomputing, vol. 81, pp. 86-96, 2012.
15. R. Savitha, S. Suresh, and N. Sundararajan, "Metacognitive learning in a Fully Complex-valued Radial Basis Function Neural Network," Neural Computation, vol. 24, no. 5, pp. 1297-1328, 2012.
16. S. Suresh and K. Subramanian, "A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems," in The International Joint Conference on Neural Networks (IJCNN), 2011, pp. 2507-2512.
17. S. Suresh, N. Sundararajan, and P. Saratchandran, "Risk-sensitive loss functions for sparse multi-category classification problems," Information Sciences, vol. 178, no. 12, pp. 2621-2638, 2008.
18. D. Marcus, T. Wang, J. Parker, J. Csernansky, J. Morris, and R. Buckner, "Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults," Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498-1507, 2007.
19. Y. Fan and D. Shen, "Integrated feature extraction and selection for neuroimage classification," in Medical Imaging 2009: Image Processing, vol. 7259, no. 1, 2009, p. 72591U.
20. W. Yang, H. Xia, B. Xia, L. M. Lui, and X. Huang, "ICA-based feature extraction and automatic classification of AD-related MRI data," in Sixth International Conference on Natural Computation (ICNC), vol. 3, aug. 2010, pp. 1261-1265.
21. SPM8, "Wellcome trust center for neuroimaging," Institute of neurlogy, UCL, London, UK, 2011, http://www.fil.ion.ucl.ac.uk/spm/.
22. J. S. Hugo Gavert, Jarmo Hurri and A. Hyvarinen, "The FastICA package for MATLAB," 2005, http://www.cis.hut.fi/projects/ica/fastica/.
23. J. Ashburner and K. Friston, "Unified segmentation," NeuroImage, vol. 26, pp. 839-851, 2005.
24. A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626-634, 1999.
25. S. Suresh, N. Sundararajan, and P. Saratchandran, "A sequential multicategory classifier using radial basis function networks," Neurocomputing, vol. 71, no. 1, pp. 1345-1358, 2008.