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Meta-Cognitive Learning Neural Classifier for Alzheimer’s Disease Detection

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

In this paper, we present an approach for Alzheimer’s Disease (AD) detection from Magnetic Resonance Images (MRI) using Meta-cognitive Radial Basis Function Network (McRBFN) classifier. The McRBFN classifier uses Voxel Based Morphometric (VBM) features extracted from MRI and employs a sequential Projection Based Learning (PBL) algorithm for classification. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. The study has been conducted using the well-known Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We compared the performance of the proposed classifier with reported results of existing classifiers in the literature. The performance results clearly indicates the better performance of PBL-McRBFN classifier for AD detection.

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References

  1. Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: Alzheimer’s disease and models of computation: imaging, classification, and neural models. J. Alzheimer’s Dis. 7, 187–199 (2005)

    MATH  Google Scholar 

  2. Barker, W.W., Luis, C.A., Kashuba, A., Luis, M., Harwood, D.G., Loewenstein, D., Waters, C., Jimison, P., Shepherd, E., Sevush, S., Graff-Radford, N., Newland, D., Todd, M., Miller, B., Gold, M., Heilman, K., Doty, L., Goodman, I., Robinson, B., Pearl, G., Dickson, D., Duara, R.: Relative frequencies of Alzheimer disease, Lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the state of Florida brain bank. Alzheimer Dis. Assoc. Disord. 16, 203–212 (2002)

    Article  Google Scholar 

  3. Segovia, F., Górriz, J.M., Ramírez, J., Salas-González, D., Álvarez, I.: Early diagnosis of Alzheimer’s disease based on partial least squares and support vector machine. Expert Syst. Appl. 40, 677–683 (2013)

    Article  Google Scholar 

  4. Charlon, Y., Fourty, N., Bourennane, W., Campo, E.: Design and evaluation of a device worn for fall detection and localization: application for the continuous monitoring of risks incurred by dependents in an alzheimer’s care unit. Expert Syst. Appl. 40, 7316–7330 (2013)

    Article  MATH  Google Scholar 

  5. Jack Jr, C.R., Petersen, R.C., Xu, Y.C., ÓBrien, P.C., Smith, G.E., Ivnik, R.J., Boeve, B.F., Waring, S.C., Tangalos, E.G., Kokmen, E.: Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52, 1397–1403 (1999)

    Article  Google Scholar 

  6. Killiany, R.J., Hyman, B.T., Gomez-Isla, T., Moss, M.B., Kikinis, R., Jolesz, F., Tanzi, R., Jones, K., Albert, M.S.: MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology 58, 1188–1196 (2002)

    Article  Google Scholar 

  7. Fornito, A., Yücel, M., Wood, S.J., Adamson, C., Velakoulis, D., Saling, M.M., McGorry, P.D., Pantelis, C.: Surface-based morphometry of the anterior cingulate cortex in first episode schizophrenia. Hum. Brain Mapp. 29, 478–489 (2008)

    Article  Google Scholar 

  8. Ashburner, J., Hutton, C., Frackowiak, R.S.J., Johnsrude, I., Price, C., Friston, K.J.: Identifying global anatomical differences: deformation-based morphometry. NeuroImage 6, 348–357 (1998)

    Google Scholar 

  9. Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. NeuroImage 11, 805–821 (2000)

    Article  Google Scholar 

  10. Isaacson, R., Fujita, F.: Metacognitive knowledge monitoring and self-regulated learning: academic success and reflections on learning. J. Sch. Teach. Learn. 6, 39–55 (2006)

    Google Scholar 

  11. Nelson, T.O., Narens, L.: Metamemory: a theoretical framework and new findings. Psychol. Learn. Motiv. 26, 125–173 (1990)

    Article  Google Scholar 

  12. Subramanian, K., Sundaram, S., Sundararajan, N.: A meta-cognitive neuro-fuzzy inference system (McFIS) for sequential classification problems. IEEE Trans. Fuzzy Syst. 21, 1080–1095 (2013)

    Article  Google Scholar 

  13. Sateesh Babu, G., Suresh, S., Mahanand, B.S.: Meta-cognitive q-Gaussian RBF network for binary classification: Application to mild cognitive impairment (MCI). In: The International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2013)

    Google Scholar 

  14. Sateesh Babu, G., Suresh, S.: Metacognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81, 86–96 (2011)

    Article  Google Scholar 

  15. Suresh, S., Savitha, R., Sundararajan, N.: A sequential learning algorithm for complex valued self regulating resource allocation network- CSRAN. IEEE Trans. Neural Netw. 22, 1061–1072 (2011)

    Article  Google Scholar 

  16. Savitha, R., Suresh, S., Sundararajan, N.: Metacognitive learning in a fully complex-valued radial basis function neural network. Neural Comput. 24, 1297–1328 (2012)

    Article  MathSciNet  Google Scholar 

  17. Savitha, R., Suresh, S., Sundararajan, N.: A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Netw. 32, 209–218 (2012)

    Article  MATH  Google Scholar 

  18. Subramanian, K., Suresh, S.: A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Appl. Soft Comput. 12, 3603–3614 (2012)

    Article  Google Scholar 

  19. Subramanian, K., Savitha, R., Suresh, S.: A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing 123, 110–120 (2014)

    Article  Google Scholar 

  20. Subramanian, K., Das, A.K., Suresh, S., Savitha, R.: A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm. Evolving Syst. 5(4), 219–230 (2014). doi:10.1007/s12530-013-9102-9

    Article  Google Scholar 

  21. Sateesh Babu, G., Suresh, S.: Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems. Appl. Soft Comput. 13, 654–666 (2013)

    Article  Google Scholar 

  22. Sateesh Babu, G., Suresh, S., Mahanand, B.S.: Alzheimer’s disease detection using a projection based learning meta-cognitive RBF network. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 408–415 (2012)

    Google Scholar 

  23. Sateesh Babu, G., Suresh, S., Uma Sangumathi, K., Kim, H.J.: A projection based learning meta-cognitive RBF network classifier for effective diagnosis of Parkinson’s disease. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 611–620. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  24. Sateesh Babu, G., Suresh, S.: Parkinson’s disease prediction using gene expression - a projection based learning meta-cognitive neural classifier approach. Expert Syst. Appl. 40, 1519–1529 (2013)

    Article  Google Scholar 

  25. Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Jennifer, L.W., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Gunnar, K., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691 (2008)

    Article  Google Scholar 

  26. Mahanand, B.S., Suresh, S., Sundararajan, N., Kumar, M.A.: Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network. Neural Netw. 32, 313–322 (2012)

    Article  Google Scholar 

  27. Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839–851 (2005)

    Article  Google Scholar 

  28. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1994)

    Article  Google Scholar 

  29. Sateesh Babu, G., Suresh, S.: Meta-cognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81, 86–96 (2012)

    Article  Google Scholar 

  30. Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C.: Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. NeuroImage 48, 138–149 (2009)

    Article  Google Scholar 

  31. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56, 766–781 (2011)

    Article  Google Scholar 

  32. Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55, 856–867 (2011)

    Article  Google Scholar 

  33. Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., Lötjönen, J.: The Alzheimer’s disease neuroimaging initiative: multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS ONE 6, e25446 (2011)

    Article  Google Scholar 

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Acknowledgment

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Medpace, Inc.; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. The above research was also supported by NIH grants P30 AG010129 and K01 AG030514.

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Authors and Affiliations

  1. Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India

    B. S. Mahanand

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    G. Sateesh Babu & S. Suresh

Authors
  1. B. S. Mahanand
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  2. G. Sateesh Babu
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  3. S. Suresh
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Correspondence to B. S. Mahanand .

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Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Mahanand, B.S., Babu, G.S., Suresh, S. (2015). Meta-Cognitive Learning Neural Classifier for Alzheimer’s Disease Detection. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_52

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

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