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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 8

 HIGHLY CITED PAPER 

An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features

MALIK, F. See more information about MALIK, F. on SCOPUS See more information about MALIK, F. on IEEExplore See more information about MALIK, F. on Web of Science, FARHAN, S. See more information about  FARHAN, S. on SCOPUS See more information about  FARHAN, S. on SCOPUS See more information about FARHAN, S. on Web of Science, FAHIEM, M. A. See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on Web of Science
 
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Download PDF pdficon (1,228 KB) | Citation | Downloads: 582 | Views: 4,556

Author keywords
biomedical image processing, computer aided diagnosis, feature extraction, image classification, pattern recognition

References keywords
alzheimer(51), disease(38), imaging(12), functional(12), fmri(12), brain(11), diagnosis(10), dementia(8), classification(8), neuroimage(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01008
Web of Science Accession Number: 000426449500008
SCOPUS ID: 85043280771

Abstract
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Full text preview
Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.


References | Cited By  «-- Click to see who has cited this paper

[1] A. S. Association, "2016 Alzheimer's disease facts and figures," Alzheimer's & Dementia, vol. 12(4), pp. 459-509, 2016.
[CrossRef] [SCOPUS Times Cited 1133]


[2] A. Golby, G. Silverberg, E. Race, S. Gabrieli, J. O'Shea, et al., "Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory," Brain, vol. 128, pp. 773-787, 2005.
[CrossRef] [Web of Science Times Cited 163] [SCOPUS Times Cited 181]


[3] M. S. Albert, S. T. DeKosky, D. Dickson, B. Dubois, H. H. Feldman, et al., "The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 270-279, 2011.
[CrossRef] [Web of Science Times Cited 4106] [SCOPUS Times Cited 4448]


[4] C. R. Jack, M. S. Albert, D. S. Knopman, G. M. McKhann, R. A. Sperling, et al., "Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & Dementia, vol. 7, pp. 257-262, 2011.
[CrossRef] [Web of Science Times Cited 841] [SCOPUS Times Cited 954]


[5] G. M. McKhann, D. S. Knopman, H. Chertkow, B. T. Hyman, C. R. Jack, et al., "The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 263-269, 2011.
[CrossRef] [Web of Science Times Cited 5716] [SCOPUS Times Cited 6190]


[6] R. A. Sperling, P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, et al., "Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease," Alzheimer's & dementia, vol. 7, pp. 280-292, 2011.
[CrossRef] [Web of Science Times Cited 3194] [SCOPUS Times Cited 3475]


[7] S. G. Mueller, M. W. Weiner, L. J. Thal, R. C. Petersen, C. R. Jack, et al., "Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI)," Alzheimer's & Dementia, vol. 1, pp. 55-66, 2005.
[CrossRef] [SCOPUS Times Cited 578]


[8] K. A. Johnson, N. C. Fox, R. A. Sperling, and W. E. Klunk, "Brain imaging in Alzheimer disease," Cold Spring Harbor perspectives in medicine, vol. 2, p. a006213, 2012.
[CrossRef] [Web of Science Times Cited 201] [SCOPUS Times Cited 261]


[9] L. Mosconi, M. Brys, L. Glodzik-Sobanska, S. De Santi, H. Rusinek, et al., "Early detection of Alzheimer's disease using neuroimaging," Experimental gerontology, vol. 42, pp. 129-138, 2007.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 117]


[10] J. R. Petrella, R. E. Coleman, and P. M. Doraiswamy, "Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future 1," Radiology, vol. 226, pp. 315-336, 2003.
[CrossRef] [Web of Science Times Cited 244] [SCOPUS Times Cited 292]


[11] S. Farhan, M. A. Fahiem, and H. Tauseef, "An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images," Computational and mathematical methods in medicine, vol. 2014, 2014.

[12] M. D'Esposito, E. Zarahn, G. K. Aguirre, and B. Rypma, "The effect of normal aging on the coupling of neural activity to the bold hemodynamic response," Neuroimage, vol. 10, pp. 6-14, 1999.
[CrossRef] [Web of Science Times Cited 344] [SCOPUS Times Cited 381]


[13] R. L. Buckner, A. Z. Snyder, A. L. Sanders, M. E. Raichle, and J. C. Morris, "Functional brain imaging of young, nondemented, and demented older adults," 2006

[14] S. M. Smith, P. M. Matthews, and P. Jezzard, Functional MRI: an introduction to methods: Oxford university press, 2001

[15] S. S. Bassett, D. M. Yousem, C. Cristinzio, I. Kusevic, M. A. Yassa, et al., "Familial risk for Alzheimer's disease alters fMRI activation patterns," Brain, vol. 129, pp. 1229-1239, 2006.
[CrossRef] [Web of Science Times Cited 113] [SCOPUS Times Cited 119]


[16] S. Y. Bookheimer, M. H. Strojwas, M. S. Cohen, A. M. Saunders, M. A. Pericak-Vance, et al., "Patterns of brain activation in people at risk for Alzheimer's disease," New England journal of medicine, vol. 343, pp. 450-456, 2000.
[CrossRef] [Web of Science Times Cited 908] [SCOPUS Times Cited 999]


[17] K. R. Thulborn, C. Martin, and J. T. Voyvodic, "Functional MR imaging using a visually guided saccade paradigm for comparing activation patterns in patients with probable Alzheimer's disease and in cognitively able elderly volunteers," American Journal of Neuroradiology, vol. 21, pp. 524-531, 2000.

[18] R. L. Buckner, "Event-related fMRI and the hemodynamic response," Human brain mapping, vol. 6, pp. 373-377, 1998.
[CrossRef] [SCOPUS Times Cited 219]


[19] A. C. Burggren and S. Y. Bookheimer, "Structural and functional neuroimaging in Alzheimer's disease: an update," Current topics in medicinal chemistry, vol. 2, pp. 385-393, 2002.
[CrossRef] [SCOPUS Times Cited 19]


[20] S.-J. Li, Z. Li, G. Wu, M.-J. Zhang, M. Franczak, et al., "Alzheimer disease: evaluation of a functional mr imaging index as a marker 1," Radiology, vol. 225, pp. 253-259, 2002.
[CrossRef] [Web of Science Times Cited 221] [SCOPUS Times Cited 233]


[21] G. Grön, D. Bittner, B. Schmitz, A. P. Wunderlich, and M. W. Riepe, "Subjective memory complaints: objective neural markers in patients with Alzheimer's disease and major depressive disorder," Annals of neurology, vol. 51, pp. 491-498, 2002.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 105]


[22] M. Grossman, P. Koenig, C. DeVita, G. Glosser, P. Moore, et al., "Neural basis for verb processing in Alzheimer's disease: an fMRI study," Neuropsychology, vol. 17, p. 658, 2003.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 36]


[23] C. Lustig, A. Z. Snyder, M. Bhakta, K. C. O'Brien, M. McAvoy, et al., "Functional deactivations: change with age and dementia of the Alzheimer type," Proceedings of the National Academy of Sciences, vol. 100, pp. 14504-14509, 2003.
[CrossRef] [Web of Science Times Cited 516] [SCOPUS Times Cited 551]


[24] R. Sperling, J. Bates, E. Chua, A. Cocchiarella, D. Rentz, et al., "fMRI studies of associative encoding in young and elderly controls and mild Alzheimer's disease," Journal of Neurology, Neurosurgery & Psychiatry, vol. 74, pp. 44-50, 2003.
[CrossRef] [Web of Science Times Cited 307] [SCOPUS Times Cited 351]


[25] M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, "Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 4637-4642, 2004.
[CrossRef] [Web of Science Times Cited 2365] [SCOPUS Times Cited 2523]


[26] J. R. Petrella, L. Wang, S. Krishnan, M. J. Slavin, S. E. Prince, et al., "Cortical Deactivation in Mild Cognitive Impairment: High-Field-Strength Functional MR Imaging 1," Radiology, vol. 245, pp. 224-235, 2007.
[CrossRef] [Web of Science Times Cited 112] [SCOPUS Times Cited 122]


[27] K. Supekar, V. Menon, D. Rubin, M. Musen, and M. D. Greicius, "Network analysis of intrinsic functional brain connectivity in Alzheimer's disease," PLoS Comput Biol, vol. 4, p. e1000100, 2008.
[CrossRef] [Web of Science Times Cited 707] [SCOPUS Times Cited 757]


[28] M. Liu, D. Zhang, D. Shen, and A. s. D. N. Initiative, "Ensemble sparse classification of Alzheimer's disease," NeuroImage, vol. 60, pp. 1106-1116, 2012.
[CrossRef] [Web of Science Times Cited 171] [SCOPUS Times Cited 195]


[29] N. Belmokhtar and N. Benamrane, "Classification of Alzheimer's disease from 3D structural MRI data," Age, vol. 78, pp. 69-96, 2012.

[30] E. Dinesh, M. S. Kumar, M. Vigneshwar, and T. Mohanraj, "Instinctive classification of Alzheimer's disease using FMRI, pet and SPECT images," in Intelligent Systems and Control (ISCO), 2013 7th International Conference on, 2013, pp. 405-409.
[CrossRef] [SCOPUS Times Cited 9]


[31] S. Farhan, M. A. Fahiem, F. Tahir, and H. Tauseef, "A Comparative Study of Neuroimaging and Pattern Recognition Techniques for Estimation of Alzheimer's," Life Science Journal, vol. 10, 2013.

[32] J. Ashburner, G. Barnes, C. Chen, J. Daunizeau, G. Flandin, et al., "SPM8 manual," Functional Imaging Laboratory, Institute of Neurology, 2008, 41, pp. 25-58.

[33] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, et al., "Automated multi-modality image registration based on information theory," in Information processing in medical imaging, 1995, pp. 263-274.

[34] E. E. Tripoliti, D. I. Fotiadis, and M. Argyropoulou, "A supervised method to assist the diagnosis of Alzheimer's disease based on functional magnetic resonance imaging," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 3426-3429.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 12]


[35] E. E. Tripoliti, D. I. Fotiadis, M. Argyropoulou, and G. Manis, "A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data," Journal of biomedical informatics, vol. 43, pp. 307-320, 2010.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 54]


[36] S. Sandor and R. Leahy, "Surface-based labeling of cortical anatomy using a deformable atlas," IEEE transactions on medical imaging, vol. 16, pp. 41-54, 1997.
[CrossRef] [Web of Science Times Cited 190] [SCOPUS Times Cited 214]


[37] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, "Magnetic resonance image tissue classification using a partial volume model," NeuroImage, vol. 13, pp. 856-876, 2001.
[CrossRef] [Web of Science Times Cited 675] [SCOPUS Times Cited 749]


[38] J. D. Lee, S. C. Su, C. H. Huang, W. C. Xu, and Y. Y. Wei, "Using Volume Features and Shape Features for Alzheimer's Disease Diagnosis," in Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on, 2009, pp. 437-440.
[CrossRef] [SCOPUS Times Cited 3]


[39] D. Schmitter, A. Roche, B. Maréchal, D. Ribes, A. Abdulkadir, et al., "An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease," NeuroImage: Clinical, vol. 7, pp. 7-17, 2015.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 85]


[40] S.-T. Yang, J.-D. Lee, C.-H. Huang, J.-J. Wang, W.-C. Hsu, et al., "Computer-Aided Diagnosis of Alzheimer's Disease Using Multiple Features with Artificial Neural Network," in PRICAI 2010: Trends in Artificial Intelligence: 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010. Proceedings, B.-T. Zhang and M. A. Orgun, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 699-705.
[CrossRef] [SCOPUS Times Cited 8]


[41] W. H. Nailon, "Texture analysis methods for medical image characterisation. In Biomedical imaging, InTech, 2010.
[CrossRef]


[43] A. M. Hansen, D. Jeske, and W. Kirsch, "A chi-square goodness-of-fit test for autoregressive logistic regression models with applications to patient screening," Journal of biopharmaceutical statistics, vol. 25, pp. 89-108, 2015.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[44] L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, pp. 1-39, 2010.
[CrossRef] [Web of Science Times Cited 966] [SCOPUS Times Cited 1199]


[45] T. G. Dietterich, "Ensemble methods in machine learning," in International workshop on multiple classifier systems, 2000, pp. 1-15.
[CrossRef] [Web of Science Times Cited 2564] [SCOPUS Times Cited 3041]


[46] S. Sarraf and G. Tofighi, "Deep Learning-based Pipeline to Recognize Alzheimer' s Disease using fMRI Data," bioRxiv, p. 066910, 2016.

[47] A. Khazaee, A. Ebrahimzadeh, A. Babajani-Feremi, and A. s. D. N. Initiative, "Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI," Behavioural Brain Research, 2016.

[48] E. Challis, P. Hurley, L. Serra, M. Bozzali, S. Oliver, et al., "Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI," NeuroImage, vol. 112, pp. 232-243, 2015.
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 90]


[49] R. Sampath and A. Saradha, "Alzheimer," Research Journal of Applied Sciences, Engineering and Technology, vol. 10, pp. 29-34, 2015.
[CrossRef] [SCOPUS Times Cited 3]


[50] A. Khazaee, A. Ebrahimzadeh, and A. Babajani-Feremi, "Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory," in Biomedical Engineering (ICBME), 2014 21th Iranian Conference on, 2014, pp. 252-257.
[CrossRef] [SCOPUS Times Cited 5]


[51] C.-Y. Wee, P.-T. Yap, D. Zhang, K. Denny, J. N. Browndyke, et al., "Identification of MCI individuals using structural and functional connectivity networks," Neuroimage, vol. 59, pp. 2045-2056, 2012.
[CrossRef] [Web of Science Times Cited 212] [SCOPUS Times Cited 234]


[52] Z. Dai, C. Yan, Z. Wang, J. Wang, M. Xia, et al., "Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)," Neuroimage, vol. 59, pp. 2187-2195, 2012.
[CrossRef] [Web of Science Times Cited 173] [SCOPUS Times Cited 182]


[53] E. E. Tripoliti, D. I. Fotiadis, and M. Argyropoulou, "An automated supervised method for the diagnosis of Alzheimer's disease based on fMRI data using weighted voting schemes," in International Workshop on Imaging Systems and Techniques, 2008.
[CrossRef] [SCOPUS Times Cited 11]




References Weight

Web of Science® Citations for all references: 25,442 TCR
SCOPUS® Citations for all references: 30,140 TCR

Web of Science® Average Citations per reference: 480 ACR
SCOPUS® Average Citations per reference: 569 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-09-29 12:51 in 286 seconds.




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