Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease

International Journal of Neural Systems

Volumn 26, Issue 7, 2016, Pages

  Ortiz, Andrés ^a   Munilla, Jorge ^a   Górriz, Juan M ^b   Ramírez, Javier ^b  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Alzheimer's disease classification; Deep learning; ensemble

Indexed keywords

BRAIN; DIAGNOSIS; NETWORK ARCHITECTURE; NEUROIMAGING;

CLASSIFICATION METHODS; CLASSIFICATION RESULTS; COMPUTER AIDED DIAGNOSIS(CAD); DEEP LEARNING; DISCRIMINATIVE FEATURES; DISEASE CLASSIFICATION; ENSEMBLE; MILD COGNITIVE IMPAIRMENTS (MCI);

COMPUTER AIDED DIAGNOSIS;

AGED; ALZHEIMER DISEASE; AREA UNDER THE CURVE; AUTOMATED PATTERN RECOGNITION; BRAIN; CLASSIFICATION; COGNITIVE DEFECT; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; DISEASE COURSE; EARLY DIAGNOSIS; FEMALE; GRAY MATTER; HUMAN; INFORMATION PROCESSING; MALE; POSITRON EMISSION TOMOGRAPHY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; THREE DIMENSIONAL IMAGING; UNSUPERVISED MACHINE LEARNING;

AGED; ALZHEIMER DISEASE; AREA UNDER CURVE; BRAIN; COGNITIVE DYSFUNCTION; DATASETS AS TOPIC; DISEASE PROGRESSION; EARLY DIAGNOSIS; FEMALE; GRAY MATTER; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MALE; PATTERN RECOGNITION, AUTOMATED; POSITRON-EMISSION TOMOGRAPHY; ROC CURVE; UNSUPERVISED MACHINE LEARNING;

EID: 84980348164     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065716500258     Document Type: Conference Paper

References (82)

1. M. P. Murphy and H. LeVine, Alzheimers disease and the β-amyloid peptide, J. Alzheimer's Dis. 19(1) (2010) 311-318.
2. Alzheimer's Disease Society, Factsheet: Drug treatments for Alzheimer's Disease (London, UK, 2014), https://www.alzheimers.org.uk. Last accessed May 2016.
3. J. Ramirez, R. Chaves, J. M. Gorriz, M. Lopez, I. A. Alvarez, D. Salas-Gonzalez, F. Segovia and P. Padilla, Computer aided diagnosis of the Alzheimer's disease combining spect-based feature selection and random forest classifiers, in Proc. IEEE Nuclear Science Symp. Conf. Record (NSS/MIC) (2009), pp. 2738-2742.
4. J. M. Górriz, F. Segovia, J. Ramírez, A. Lassl and D. Salas-González, Gmm based spect image classification for the diagnosis of Alzheimer's disease, Appl. Soft Comput. 11 (2011) 2313-2325.
5. M. López, J. Ramírez, J. M. Górriz, I. Álvarez, D. Salas-González, F. Segovia, R. Chaves, P. Padilla and M. Gómez-Río, Principal component analysisbased techniques and supervised classification schemes for the early detection of Alzheimer's disease, Neurocomputing 74(8) (2011) 1260-1271.
6. I. Álvarez, J. M. Gorriz, J. Ramirez, D. Salas- Gonzalez, M. Lopez, F. Segovia, R. Chaves, M. Gomez-Rio and C. Garcia-Puntonet, 18f-fdg pet imaging analysis for computer aided Alzheimer's diagnosis, Inform. Sci. 184(4) (2011) 903-196.
7. F. Segovia, J. M. Górriz, J. Ramírez, D. Salas- González, I. Álvarez, M. López and R. Chaves, The Alzheimer's disease neuroimaging initiative, A comparative study of the feature extraction methods for the diagnosis of Alzheimer's disease using the adni database, Neurocomputing 75 (2012) 64-71.
8. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer's disease and models of computation: Imaging, classification, and neural models, J. Alzheimer's Dis. 7(3) (2005) 187-199.
9. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer's Disease: Models of computation and analysis of EEGs, Clin. EEG Neurosci. 36(3) (2005) 131-140.
10. A. H. Ahmadlou, M. and A. Adeli, New diagnostic EEG markers of the Alzheimer's disease using visibility graph, J. Neural Transm. 117(9) (2010) 1099-1109.
11. Z. Sankari and H. Adeli, Intrahemispheric, interhemispheric and distal EEG coherence in Alzheimer's disease, Clin. Neurophysiol. 122(5) (2011) 897-906.
12. F. C. Morabito, M. Campolo, D. Labate, G. Morabito, L. Bonanno, A. Bramanti, S. de Salvo, A. Marra and P. Bramanti, A longitudinal EEG study of Alzheimer's disease progression based on a complex network approach, Int. J. Neural Syst. 25(2) (2015) 1-18.
13. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease, Neurosci. Lett. 444(2) (2008) 190-194.
14. H. Adeli and S. Ghosh-Dastidar, Automated EEGBased Diagnosis of Neurological Disorders - Inventing the Future of Neurology (CRC Press, Taylor & Francis, Boca Raton, Florida, 2010).
15. A. H. Ahmadlou and A. Adeli, Fractality and a wavelet-chao methodology for EEG-based diagnosis of Alzheimer's disease, Alzheimer Dis. Assoc. Disord. 25(1) (2011) 85-92.
16. Z. Sankari and H. Adeli, Probabilistic neural networks for EEG-based diagnosis of Alzheimer's disease using conventional and wavelet coherence, J. Neurosci. Methods 197(1) (2011) 165-170.
17. Z. Sankari and H. Adeli, Wavelet coherence model for diagnosis of Alzheimer's disease, Clin. EEG Neurosci. 43(3) (2012) 268-278.
18. A. Ortiz, J. M. Górriz, J. Ramírez and F. J. Martínez-Murcia, Automatic ROI selection in structural brain MRI using som 3d projection, PLOS One 9(4) (2014) 1-12.
19. A. Ortiz, J. M. Górriz, J. Ramírez and F. J. Martínez-Murcia, LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease, Pattern Recogn. Lett. 34(14) (2013) 1725-1733.
20. D. Chyzhyk, M. Graña, A. Savio and J. Maiora, Hybrid dendritic computing with kernel-lica applied to Alzheimer's disease detection in MRI, Neurocomputing 75(1) (2012) 72-77.
21. R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M. Habert, M. Chupin, H. Benali and O. Colliot, Alzheimer's disease neuroimaging initiative, automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the adni database, Neuroimage 56(2) (2010) 766-781.
22. M. Liu, D. Zhang and D. Shen, Alzheimer's disease neuroimaging initiative, ensemble sparse classification of Alzheimer's disease, Neuroimage 60(2) (2012) 1106-1116.
23. A. Savio and M. Graa, An ensemble of classifiers guided by the aal brain atlas for Alzheimers disease detection, in Advances in Computational Intelligence, eds. I. Rojas, G. Joya and J. Cabestany, Lecture Notes in Computer Science, Vol. 7903 (Springer Berlin Heidelberg, 2013), pp. 107-114.
24. J. Dukart, K. Mueller, H. Barthel, A. Villringer, O. Sabri and M. L. Schroeter, Alzheimer's Disease Neuroimaging Initiative.
25. C. J. Jack, R. Petersen, X. Y.C., P. O'Brien, G. Smith, R. Ivnik, B. Boeve, S. Waring, E. Tangalos and E. Kokmen, Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment, Neurology 52(7) (1999) 1397-1403.
26. K. A. Lin and P. M. Doraiswamy, When mars versus venus is not a clich: Gender differences in the neurobiology of Alzheimers disease, Front. Neurol. 5(288) (2015) 1-3.
27. C. Plant, S. J. Teipel, A. Oswald, C. Bhm, T. Meindl, J. Mourao-Miranda, A. W. Bokde, H. Hampel and M. Ewers, Automated detection of brain atrophy patterns based on {MRI} for the prediction of alzheimer's disease, Neuroimage 50(1) (2010) 162-174.
28. S. J. Teipel, C. Born, M. Ewers, A. L. Bokde, M. F. Reiser, H.-J. Mller and H. Hampel, Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment, Neuroimage 38(1) (2007) 13-24.
29. J. Barnes, G. R. Ridgway, J. Bartlett, S. M. Henley, M. Lehmann, N. Hobbs, M. J. Clarkson, D. G. Mac- Manus, S. Ourselin and N. C. Fox, Head size, age and gender adjustment in {MRI} studies: A necessary nuisance?, Neuroimage 53(4) (2010) 1244-1255.
30. J. Ashburner and T. Group, SPM8 Manuel, Vol. 12 (Functional Imaging Laboratory, Institute of Neurology, UK, 2011).
31. Structural Brain Mapping Group, Department of Psychiatry, Available at http://dbm.neuro.unijena. de/vbm8/VBM8-Manual.pdf [Accessed on 10 March 2014].
32. N. S. V. Villemagne, S. Berlangieri, S. Lee, M. Cherk, S. Gong, U. Ackermann, T. Saunder, H. Tochon- Danguy, G. Jones, C. Smith, G. O'Keefe, C. Masters and C. Rowe, Visual assessment versus quantitative assessment of 11c-pib pet and 18f-fdg pet for detection of Alzheimer's disease, J. Nucl. Med. 48(4) (2004) 34-41.
33. D. Perani, V. D. Nero, G. Vallar, S. Cappa, C. Messa, G. Bottini, A. Berti, D. Passafiume, G. Scarlato, P. Gerundini, G. L. Lenzi and F. Fazi, Technetium- 99m hm-pao-spect study of regional cerebral perfusion in early Alzheimer's disease, J. Nucl. Med. 29(9) (1988) 1507-1514.
34. G. Giovacchini, A. Lerner, M. Maria, T. Toczek, M. C. Fraser, K. Ma, M. James, C. Demar, P. P. Herscovitch, M. William, C. Eckelman, S. I. Rapoport and R. E. Carson, Brain incorporation of 11c-arachidonic acid, blood volume, and blood flow in healthy aging: A study with partialvolume correction, J. Nucl. Med. 45(9) (2004) 1471-1479.
35. J. Aston, V. Cunningham, M. Asselin, A. Hammers, A. Evans and R. N. Gunn, Positron emission tomography partial volume correction: Estimation and algorithms, J. Cereb. Blood Flow Metab. 22 (2002) 1019-1034.
36. W. Jagust, D. Bandy, K. Chen, N. L. Foster, S. M. Landau and C. A. Mathis and the ADNI Investigators, The ADNI core PET, Alzheimer's Dementia J. Alzheimer's Assoc. 6(3) (2010) 221-229.
37. S. J. Teipel, J. Kurth, B. Krause and M. J. Grothe, Alzheimer's Disease Neuroimaging Initiative, The relative importance of imaging markers for the prediction of alzheimer's disease dementia in mild cognitive impairment - beyond classical regression, Neuroimage Clin. 8 (2015) 583-593.
38. G. Flandin, F. Kherif, X. Pennec, D. Riviere, N. Ayache and J.-B. Poline, fMRI data analysis, Proceedings of the IEEE Int. Symposium on Biomedical Imaging (7-11 July 2002, Prague, Czech Republic), pp. 907-910.
39. A. Minoshima, N. Foster and D. Kuhl, Metabolic reduction in the posterior cinculate cortex in very early Alzheimer's disease, Lancet 3440(8926) (1994) 895.
40. P. J. Nestor, P. Scheltens and J. R. Hodges, Advances in the early detection of Alzheimer's disease, Nat. Rev. Neurosci. 5(1) (2004) 34-41.
41. S. Huang, J. Li, L. Sun, J. Ye, A. Fleisher, T. Wu, K. Chen, E. Reiman and A. D. N. Initiative, Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation, Neuroimage 50(3) (2010) 935-949.
42. G. E. Hinton and S. Osindero, A fast learning algorithm for deep belief nets, Neural Comput. 18 (2006) 2006.
43. V. N. Vapnik, Statistical Learning Theory (Wiley- Interscience, 1998).
44. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, U. D. Montral and M. Qubec, Greedy layer-wise training of deep networks, in NIPS (MIT Press, 2007), pp. 153-160.
45. C. Poultney, S. Chopra and Y. Lecun, Efficient learning of sparse representations with an energy-based model, Advances in Neural Information Processing Systems (NIPS 2006), (MIT Press, 2006), pp. 1137- 1144.
46. S. Dura-Bernal, G. Garreau, A. Andreou, S. Denham and T. wennekers, Multimodal integration of micro-doppler sonar and auditory signals for behavior classification with convolutional networks, Int. J. Neural Syst. 13(1) (2013) 1-15.
47. P. Smolensky, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1 (MIT Press, Cambridge, MA, USA, 1986), pp. 194-281.
48. Y. Bengio, Learning deep architectures for ai, Found. Trends Mach. Learn. 2 (2009) 1-127.
49. M. Welling, M. Rosen-Zvi and G. E. Hinton, Exponential family harmoniums with an application to information retrieval, Vol. 17, Neural Inform. Process. Syst. (NIPS) (2004), pp. 1481-1488.
50. G. Hinton, Training products of experts by minimizing contrastive divergence, Neural Comput. 14 (2000) 2002.
51. H. Adeli and S.-L. Hung, Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems (JohnWiley & Sons, Inc., New York, NY, USA, 1994).
52. R. Rojas, Neural Networks: A Systematic Introduction (Springer-Verlag New York, Inc., New York, NY, USA, 1996).
53. M. Aurelio Ranzato, Y. lan Boureau and Y. L. Cun, Sparse feature learning for deep belief networks, in Advances in Neural Information Processing Systems, Vol. 20, eds. J. Platt, D. Koller, Y. Singer and S. Roweis (Curran Associates, Inc., 2008), pp. 1185- 1192.
54. H. Lee, P. Pham, Y. Largman and A. Y. Ng, Unsupervised feature learning for audio classification using convolutional deep belief networks, in Advances in Neural Information Processing Systems, Vol. 22, eds. Y. Bengio, D. Schuurmans, J. Lafferty, C.Williams and A. Culotta (Curran Associates, Inc., 2009), pp. 1096-1104.
55. H. Suk and D. Shen, Deep learning-based feature representation for AD/MCI classification., Medical image computing and computer-assisted intervention, Int. Conf. Medical Image Computing and Computer-Assisted Intervention -MICCAI 2013 (2013), pp. 583-590.
56. D. Eck and U. D. Montr'eal, Learning features from music audio with deep belief networks, 11th Int. Society for Music Information Retrieval Conf. (ISMIR, 2010), pp. 339-344.
57. Y. Liu, S. Zhou and Q. Chen, Discriminative deep belief networks for visual data classification, Pattern Recogn. 44 (2011) 2287-2296.
58. C. Sammut and G. I. Webb, Statistical Learning Theory (Springer, 2010).
59. Y. Zhang and W. Zhou, Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial, Int. J. Neural Syst. 25(6) (2015) 1-14.
60. E. Castillo, D. Peteiro-Barral, B. Guijarro Berdinas and O. Fontenla-Romero, Distributed one-class support vector machine, Int. J. Neural Syst. 25(7) (2015) 1-17.
61. A. Hidalgo-Munñoz, J. Górriz, J. Ramírez and P. Padilla, Regions of interest computed by svm wrapped method for Alzheimers disease examination from segmented mri, Front. Aging Neurosci. 6(20) (2014).
62. J. Kittler, M. Hatef, R. Duin and J. Matas, On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell. 20 (1998) 226-239.
63. R. Duin, Classifiers in almost empty spaces, Proceedings 15th Int. Conf. Pattern Recognition, Vol. 2 (2000), pp. 1-7.
64. G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Trans. Inf. Theor. 14 (2006) 55-63.
65. A. Savio and M. Graña, An ensemble of classifiers guided by the AAL brain atlas for Alzheimer's disease detection, in Advances in Computational Intelligence - 12th International Work-Conf. Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part II (Springer, 2013), pp. 107-114.
66. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, August, 2003).
67. M. Galar, A. Fernández, E. Barrenechea, H. Bustince and F. Herrera, An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recogn. 44 (2011) 1761-1776.
68. M. A. H. Akhand, M. Islam and M. Kazuyuki, A comparative study of data sampling techniques for constructing neural network ensembles, Int. J. Neural Syst. 19(2) (2009) 67-89.
69. B. Baruque, E. Corchado and Y. Hujun, The s-2 ensemble fusion algorithm, Int. J. Neural Syst. 21(6) (2011) 505-525.
70. M. Ahmadlou, A. Adeli, R. Bajo and H. Adeli, Complexity of functional connectivity networks in mild cognitive impairment patients during a working memory task, Clin. Neurophysiol. 125(4) (2014) 694-702.
71. S. Haykin, Neural Networks, 2nd edn. (Prentice-Hall, 1999).
72. H. Hebbo and J. W. Kim, Classification with deep belief networks, Project Report, TU Berlin (2013).
73. W. Navidi, Statistics for Engineers and Scientists (McGraw-Hill Science, 2010).
74. J. Stoeckel and G. Fung, Svm feature selection for classification of spect images of Alzheimer's disease using spatial information, in Proc. Fifth IEEE Int. Data Mining Conf. (2005), pp. 243-258.
75. S. Theodoridis and K. Koutroumbas, Pattern Recognition (Academic Press, 2009).
76. H. Abdi and L. Williams, Principal component analysis, Wiley Interdisciplinary Reviews: Comput. Stat. 2 (2010) 433-459.
77. D. M. W. Powers, Evaluation: From precision, recall and f-measure to ROC, informedness, markedness & correlation, J. Mach. Learning Technol. 2(1) (2011) 37-63.
78. K. P. Murphy, Machine Learning: A Probabilistic Perspective (The MIT Press, 2012).
79. T. Fawcett, An introduction to roc analysis, Pattern Recognt. Lett. 27 (2006) 861-874.
80. S. Smith and S. Cagnoni, Genetic and Evolutionary Computation: Medical Applications (Wiley Publishing, 2011).
81. C. Hinrichs, V. Singh, G. Xu and S. Johnson and the Alzheimers disease neuroimaging initiative, predictive markers for AD in multi-modality framework: An analysis of MCI progression in the ADNI population, Neuroimage 55 (2011) 574-589.
82. D. Zhang, Y. Wang, L. Zhou, H. Yuan and D. Shen, Multimodal classification of Alzheimer's disease and mild cognitive impairment, Neuroimage 55(1) (2011) 856-867.