Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders
DOI: DOI: 10.4103/jmss.JMSS_11_20
Abstract
Background: A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed. Method: The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method. Results: The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively. Conclusion: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.
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Li S, Shi F, Pu F, Li X, Jiang T, Xie S, et al. Hippocampal shape analysis of Alzheimer disease based on machine learning methods. AJNR Am J Neuroradiol 2007;28:1339-45.
Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM. Forecasting the global burden of Alzheimer's disease. Alzheimers Dement 2007;3:186-91.
Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 2008;29:514-23.
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, et al. Automatic classification of MR scans in Alzheimer's disease. Brain 2008;131:681-9.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, et al. Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 2011;56:766-81.
Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease. Brain 2007;130:2616-35.
Nordberg A, Rinne JO, Kadir A, Långström B. The use of PET in Alzheimer disease. Nat Rev Neurol 2010;6:78-87.
Dubois B, Feldman HH, Jacova C, DeKosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer's disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-46.
Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 2013;4:627-35.
Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MK, Tanik UJ, et al. Automated detection of Alzheimer's disease using brain MRI images-A study with various feature extraction techniques. J Med Syst 2019;43:302.
Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H. Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 2018;42:85.
Duin RP. Classifiers in Almost Empty Spaces. Proceedings 15th International Conference on Pattern Recognition: IEEE; 2000. p. 1-7.
Juottonen K, Laakso MP, Partanen K, Soininen H. Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. AJNR Am J Neuroradiol 1999;20:139-44.
Fox NC, Warrington EK, Freeborough PA, Hartikainen P, Kennedy AM, Stevens JM, et al. Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study. Brain 1996;119(Pt 6):2001-7.
Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, et al. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease. Arch Neurol 1993;50:949-54.
Laakso MP, Soininen H, Partanen K, Lehtovirta M, Hallikainen M, Hänninen T, et al. MRI of the hippocampus in Alzheimer's disease: Sensitivity, specificity, and analysis of the incorrectly classified subjects. Neurobiol Aging 1998;19:23-31.
Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, et al. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Neuroimage 2009;45:S3-15.
Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H, et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 2008;248:194-201.
Kantarci K. Magnetic resonance markers for early diagnosis and progression of Alzheimer's disease. Expert Rev Neurother 2005;5:663-70.
Westman E, Simmons A, Zhang Y, Muehlboeck JS, Tunnard C, Liu Y, et al. Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls. Neuroimage 2011;54:1178-87.
Maqsood M, Nazir F, Khan U, Aadil F, Jamal H, Mehmood I, et al. Transfer learning assisted classification and detection of Alzheimer's disease stages using 3D MRI scans. Sensors (Basel) 2019;19:1-19.
Islam K, Damiati S, Sethi J, Suhail A, Pan G. Development of a label-free immunosensor for clusterin detection as an Alzheimer's biomarker. Sensors (Basel) 2018;18:1-12.
Toro CA, Gonzalo Martín C, García-Pedrero A, Menasalvas Ruiz E. Supervoxels-based histon as a new Alzheimer's disease imaging biomarker. Sensors (Basel) 2018;18:1-18.
Garyfallou GZ, Ketebu O, Sahin S, Mukaetova-Ladinska EB, Catt M, Yu EH. Electrochemical detection of plasma immunoglobulin as a biomarker for Alzheimer's disease. Sensors (Basel) 2017;17:1-13.
Lahmiri S. Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance. Physica A: Statist Mechan Appl 2016;456:235-43.
Lahmiri S, Shmuel A. Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer's disease. Biomed Signal Proc Control 2019;52:414-9.
Beheshti I, Demirel H, Matsuda H. Alzheimer's disease neuroimaging initiative. classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 2017;83:109-19.
Spasov S, Passamonti L, Duggento A, Liò P, Toschi N. Alzheimer's disease neuroimaging initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage 2019;189:276-87.
Kashefpoor M, Rabbani H, Barekatain M. Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features. J Med Signals Sens 2016;6:25-32.
Catana C, Drzezga A, Heiss WD, Rosen BR. PET/MRI for neurologic applications. J Nucl Med 2012;53:1916-25.
Cheng B, Zhang D, Shen D. Domain transfer learning for MCI conversion prediction. Med Image Comput Comput Assist Interv 2012;15:82-90.
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, et al. The Alzheimer's disease neuroimaging initiative: A review of papers published since its inception. Alzheimer's and dementia: J Alzheimer's Assoc 2012;8 Suppl 1:S1-68.
Available from: https://medicine.uiowa.edu/mri/research/previous-projects. [Last accessed on 2020 Jan 14].
Poultney C, Chopra S, Cun YL. Efficient learning of sparse representations with an energy-based model. In: Advances in Neural Information Processing Systems. Advances in; 2006. p. 1137-44.
Ngiam J, Coates A, Lahiri A, Prochnow B, Ng A, Le QV. On Optimization Methods for Deep Learning. ICML-11; 2011. p. 265-72.
Hinton GE. Connectionist learning procedures. Artific Intell 1989;40:185-234.
Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. Adv Neural Inform Proc Syst 2007;19:153.
Fan Y, Resnick SM, Wu X, Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study. Neuroimage 2008;41:277-85.
Mourão-Miranda J, Bokde AL, Born C, Hampel H, Stetter M. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 2005;28:980-95.
Burges CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2:121-67.
Shawe-Taylor J, Cristianini N. Support vector machines and other kernel-Based. Learning Methods. UK: Cambridge University Press; 2000.
Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Alzheimer's disease neuroimaging initiative. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011;55:856-67.
Dai D, He H, Vogelstein JT, Hou Z. Accurate prediction of AD patients using cortical thickness networks. Mach Vis Appl 2013;24:1445-57.
Liu J, Li M, Lan W, Wu FX, Pan Y, Wang J. Classification of Alzheimer's disease using whole brain hierarchical network. IEEE/ACM Trans Comput Biol Bioinform 2018;15:624-32.
da Silva Lopes HF, Abe JM, Anghinah R. Application of paraconsistent artificial neural networks as a method of aid in the diagnosis of Alzheimer disease. J Med Syst 2010;34:1073-81.
Mishra S, Beheshti I, Khanna P. Initiative ftAsDN. A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease. Int J Imag Syst Technol 2018;28:302-14.
Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F. Early diagnosis of Alzheimer?s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 2015;151:139-50.
Lian C, Liu M, Zhang J, Shen D. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell 2020;42:880-93.
Ben Ahmed O, Benois-Pineau J, Allard M, Ben Amar C, Catheline G. Classification of Alzheimer's disease subjects from MRI using hippocampal visual features. Multimedia Tools Appl 2014;74:1249-66.
Zhou K, He W, Xu Y, Xiong G, Cai J. Feature selection and transfer learning for Alzheimer's disease clinical diagnosis. Appl Sci 2018;8:1372.
Suk HI, Lee SW, Shen D. Alzheimer's disease neuroimaging initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 2014;101:569-82.
Saravanakumar S, Thangaraj P. A computer aided diagnosis system for identifying Alzheimer's from MRI Scan using Improved Adaboost. J Med Syst 2019;43:76.
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-98.
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