research-article

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review

Authors:

C. T. LinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 1s

Article No.: 30, Pages 1 - 35

https://doi.org/10.1145/3344998

Published: 17 April 2020 Publication History

Abstract

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

References

[1]

Ahmed Abdulkadir, Bénédicte Mortamet, Prashanthi Vemuri, Clifford R. Jack Jr., Gunnar Krueger, Stefan Klöppel, Alzheimer’s Disease Neuroimaging Initiative, et al. 2011. Effects of hardware heterogeneity on the performance of SVM Alzheimer’s disease classifier. Neuroimage 58, 3 (2011), 785--792.

[2]

Mehran Ahmadlou, Hojjat Adeli, and Anahita Adeli. 2010. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Trans. 117, 9 (2010), 1099--1109.

[3]

B. Al-Naami, N. Gharaibeh, and A. AlRazzaq Kheshman. 2013. Automated detection of Alzheimer’s disease using region growing technique and artificial neural network. World Acad. Sci. Eng. Technol. Int. J. Biomed. Biol. Eng 7, 5 (2013).

[4]

Saruar Alam, Goo-Rak Kwon, and Alzheimer’s Disease Neuroimaging Initiative. 2017. Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM. Int. J. Imag. Syst. Technol. 27, 2 (2017), 133--143.

[5]

Saruar Alam, Goo-Rak Kwon, Ji-In Kim, and Chun-Su Park. 2017. Twin SVM-based classification of Alzheimer’s disease using complex dual-tree wavelet principal coefficients and LDA. J. Health. Eng. 2017 (2017).

[6]

Almir Aljović, Almir Badnjević, and Lejla Gurbeta. 2016. Artificial neural networks in the discrimination of Alzheimer’s disease using biomarkers data. In Proceedings of the 5th Mediterranean Conference on Embedded Computing (MECO’16). IEEE, 286--289.

[7]

Ignacio Álvarez, Míriam López, Juan Manuel Górriz, Javier Ramírez, Diego Salas-Gonzalez, Carlos García Puntonet, and Fermín Segovia. 2008. Automatic classification system for the diagnosis of Alzheimer’s disease using component-based SVM aggregations. In Proceedings of the International Conference on Neural Information Processing. Springer, 402--409.

[8]

Meysam Asgari, Jeffrey Kaye, and Hiroko Dodge. 2017. Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimer’s Dementia: Trans. Res. Clin. Intervent. 3, 2 (2017), 219--228.

[9]

Silvia Basaia, Federica Agosta, Luca Wagner, Elisa Canu, Giuseppe Magnani, Roberto Santangelo, Massimo Filippi, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clin. (2018), 101645.

[10]

Randall J. Bateman, Paul S. Aisen, Bart De Strooper, Nick C. Fox, Cynthia A. Lemere, John M. Ringman, Stephen Salloway, Reisa A. Sperling, Manfred Windisch, and Chengjie Xiong. 2011. Autosomal-dominant Alzheimer’s disease: A review and proposal for the prevention of Alzheimer’s disease. Alzheimer’s Res. Ther. 3, 1 (2011), 1.

[11]

Iman Beheshti, Hasan Demirel, Hiroshi Matsuda, and Alzheimer’s Disease Neuroimaging Initiative. 2017. 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. 83 (2017), 109--119.

Digital Library

[12]

Iman Beheshti, Norihide Maikusa, Morteza Daneshmand, Hiroshi Matsuda, Hasan Demirel, and Gholamreza Anbarjafari. 2017. Classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion using histogram-based analysis of patient-specific anatomical brain connectivity networks. J. Alzheimer’s Dis. 60, 1 (2017), 295--304.

[13]

Xia-an Bi, Qin Jiang, Qi Sun, Qing Shu, and Yingchao Liu. 2018. Analysis of Alzheimer’s disease based on the random neural network cluster in fMRI. Front. Neuroinform. 12 (2018), 60.

[14]

Xia-an Bi, Qing Shu, Qi Sun, and Qian Xu. 2018. Random support vector machine cluster analysis of resting-state fMRI in Alzheimer’s disease. PloS One 13, 3 (2018), e0194479.

[15]

Halil Bisgin, Tanmay Bera, Hongjian Ding, Howard G. Semey, Leihong Wu, Zhichao Liu, Amy E. Barnes, Darryl A. Langley, Monica Pava-Ripoll, Himansu J. Vyas, et al. 2018. Comparing SVM- and ANN-based machine learning methods for species identification of food contaminating beetles. Sci. Rep. 8 (2018).

[16]

Carlos Cabral, Pedro M. Morgado, Durval Campos Costa, Margarida Silveira, Alzheimer’s Disease Neuroimaging Initiative, et al. 2015. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput. Biol. Med. 58 (2015), 101--109.

Digital Library

[17]

Ramon Casanova, Fang-Chi Hsu, Kaycee M. Sink, Stephen R. Rapp, Jeff D. Williamson, Susan M. Resnick, Mark A. Espeland, and Alzheimer’s Disease Neuroimaging Initiative. 2013. Alzheimer’s disease risk assessment using large-scale machine learning methods. PLoS One 8, 11 (2013), e77949.

[18]

Sandeep Chaplot, L. M. Patnaik, and N. R. Jagannathan. 2006. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control 1, 1 (2006), 86--92.

[19]

R. Chaves, J. Ramírez, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Alvarez, and F. Segovia. 2009. SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci. Lett. 461, 3 (2009), 293--297.

[20]

Gang Chen, B. Douglas Ward, Chunming Xie, Wenjun Li, Zhilin Wu, Jennifer L. Jones, Malgorzata Franczak, Piero Antuono, and Shi-Jiang Li. 2011. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology 259, 1 (2011), 213--221.

[21]

Ying Chen and Tuan D. Pham. 2013. Development of a brain MRI-based hidden markov model for dementia recognition. Biomed. Engineer. Online 12, 1 (2013), S2.

[22]

Bo Cheng, Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang Zhang, and Alzheimer’s Disease Neuroimaging Initiative. 2017. Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15, 2 (2017), 115--132.

[23]

Bo Cheng, Mingxia Liu, Heung-Il Suk, Dinggang Shen, Daoqiang Zhang, and Alzheimer’s Disease Neuroimaging Initiative. 2015. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imag. Behav. 9, 4 (2015), 913--926.

[24]

Bo Cheng, Mingxia Liu, Daoqiang Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease. Brain Imag. Behav. (2018), 1--16.

[25]

Andrea Chincarini, Paolo Bosco, Piero Calvini, Gianluca Gemme, Mario Esposito, Chiara Olivieri, Luca Rei, Sandro Squarcia, Guido Rodriguez, Roberto Bellotti, et al. 2011. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage 58, 2 (2011), 469--480.

[26]

Youngsang Cho, Joon-Kyung Seong, Yong Jeong, Sung Yong Shin, Alzheimer’s Disease Neuroimaging Initiative, et al. 2012. Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59, 3 (2012), 2217--2230.

[27]

Darya Chyzhyk, Alexandre Savio, and Manuel Graña. 2014. Evolutionary ELM wrapper feature selection for Alzheimer’s disease CAD on anatomical brain MRI. Neurocomputing 128 (2014), 73--80.

Digital Library

[28]

David Glenn Clark, Paula M. McLaughlin, Ellen Woo, Kristy Hwang, Sona Hurtz, Leslie Ramirez, Jennifer Eastman, Reshil-Marie Dukes, Puneet Kapur, Thomas P. DeRamus, et al. 2016. Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. Alzheimer’s Dementia: Diagn. Assess. Dis. Monitor. 2 (2016), 113--122.

[29]

Corinna Cortes and Vladimir Vapnik. 1995. Support vector machine. Mach. Learn. 20, 3 (1995), 273--297.

Digital Library

[30]

Nello Cristianini and Bernhard Scholkopf. 2002. Support vector machines and kernel methods: the new generation of learning machines. AI Mag. 23, 3 (2002), 31--31.

Digital Library

[31]

Ruoxuan Cui, Manhua Liu, and Gang Li. 2018. Longitudinal analysis for Alzheimer’s disease diagnosis using RNN. In Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI’18). IEEE, 1398--1401.

[32]

Rémi Cuingnet, Emilie Gerardin, Jérôme Tessieras, Guillaume Auzias, Stéphane Lehéricy, Marie-Odile Habert, Marie Chupin, Habib Benali, Olivier Colliot, and Alzheimer’s Disease Neuroimaging Initiative. 2011. Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56, 2 (2011), 766--781.

[33]

Rémi Cuingnet, Joan Alexis Glaunès, Marie Chupin, Habib Benali, and Olivier Colliot. 2013. Spatial and anatomical regularization of SVM: A general framework for neuroimaging data. IEEE Trans. Pattern Anal. Mach. Intell. 35, 3 (2013), 682--696.

Digital Library

[34]

Helder Frederico da Silva Lopes, Jair M. Abe, and Renato Anghinah. 2010. Application of paraconsistent artificial neural networks as a method of aid in the diagnosis of Alzheimer’s disease. J. Med. Syst. 34, 6 (2010), 1073--1081.

Digital Library

[35]

Zhengjia Dai, Chaogan Yan, Zhiqun Wang, Jinhui Wang, Mingrui Xia, Kuncheng Li, and Yong He. 2012. Discriminative analysis of early Alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). Neuroimage 59, 3 (2012), 2187--2195.

[36]

Anders M. Dale, Bruce Fischl, and Martin I. Sereno. 1999. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 2 (1999), 179--194.

[37]

Christos Davatzikos, Susan M. Resnick, X. Wu, P. Parmpi, and Christopher M. Clark. 2008. Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41, 4 (2008), 1220--1227.

[38]

Rahul S. Desikan, Howard J. Cabral, Christopher P. Hess, William P. Dillon, Christine M. Glastonbury, Michael W. Weiner, Nicholas J. Schmansky, Douglas N. Greve, David H. Salat, Randy L. Buckner, et al. 2009. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain 132, 8 (2009), 2048--2057.

[39]

James D. Doecke, Simon M. Laws, Noel G. Faux, William Wilson, Samantha C. Burnham, Chiou-Peng Lam, Alinda Mondal, Justin Bedo, Ashley I. Bush, Belinda Brown, et al. 2012. Blood-based protein biomarkers for diagnosis of Alzheimer’s disease. Arch. Neurol. 69, 10 (2012), 1318--1325.

[40]

Juergen Dukart, Karsten Mueller, Henryk Barthel, Arno Villringer, Osama Sabri, Matthias Leopold Schroeter, and Alzheimer’s Disease Neuroimaging Initiative. 2013. Meta-analysis-based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psych. Res.: Neuroimag. 212, 3 (2013), 230--236.

[41]

Yong Fan, Susan M. Resnick, Xiaoying Wu, and Christos Davatzikos. 2008. Structural and functional biomarkers of prodromal Alzheimer’s disease: A high-dimensional pattern classification study. Neuroimage 41, 2 (2008), 277--285.

[42]

Julian Fritsch, Sebastian Wankerl, and Elmar Nöth. 2019. Automatic diagnosis of Alzheimer’s disease using neural network language models. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). IEEE, 5841--5845.

[43]

Rudimar L. Frozza, Mychael V. Lourenco, and Fernanda G. De Felice. 2018. Challenges for Alzheimer’s disease therapy: Insights from novel mechanisms beyond memory defects. Front. Neurosc. 12 (2018), 37.

[44]

Motonobu Fujishima, Atsushi Kawaguchi, Norihide Maikusa, Ryozo Kuwano, Takeshi Iwatsubo, and Hiroshi Matsuda. 2017. Sample size estimation for Alzheimer’s disease trials from japanese ADNI serial magnetic resonance imaging. J. Alzheimer’s Dis. 56, 1 (2017), 75--88.

[45]

Glenn Fung and Jonathan Stoeckel. 2007. SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowl. Info. Syst. 11, 2 (2007), 243--258.

Digital Library

[46]

Esteve Gallego-Jutglà, Jordi Solé-Casals, François-Benoît Vialatte, Mohamed Elgendi, Andrzej Cichocki, and Justin Dauwels. 2015. A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease. J. Neural Eng. 12, 1 (2015), 016018.

[47]

Andrés García-Floriano, Cuauhtémoc López-Martín, Cornelio Yáñez-Márquez, and Alain Abran. 2018. Support vector regression for predicting software enhancement effort. Info. Softw. Technol. 97 (2018), 99--109.

[48]

Emilie Gerardin, Gaël Chételat, Marie Chupin, Rémi Cuingnet, Béatrice Desgranges, Ho-Sung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero, et al. 2009. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47, 4 (2009), 1476--1486.

[49]

H. T. Gorji and J. Haddadnia. 2015. A novel method for early diagnosis of Alzheimer’s disease based on pseudo zernike moment from structural MRI. Neuroscience 305 (2015), 361--371.

[50]

Gábor Gosztolya, Veronika Vincze, László Tóth, Magdolna Pákáski, János Kálmán, and Ildikó Hoffmann. 2019. Identifying mild cognitive impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features. Comput. Speech Lang. 53 (2019), 181--197.

[51]

Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert, Alzheimer’s Disease Neuroimaging Initiative, et al. 2013. Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage 65 (2013), 167--175.

[52]

Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. 2002. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 1--3 (2002), 389--422.

Digital Library

[53]

Sven Haller, Pascal Missonnier, F. R. Herrmann, Cristelle Rodriguez, M.-P. Deiber, Duy Nguyen, Gabriel Gold, K.-O. Lovblad, and Panteleimon Giannakopoulos. 2013. Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI. Amer. J. Neuroradiol. 34, 2 (2013), 283--291.

[54]

Laura Hernández-Domínguez, Sylvie Ratté, Gerardo Sierra-Martínez, and Andrés Roche-Bergua. 2018. Computer-based evaluation of Alzheimer’s disease and mild cognitive impairment patients during a picture description task. Alzheimer’s Dementia: Diagn. Assess. Disease Monitor. 10 (2018), 260--268.

[55]

Kilian Hett, Vinh-Thong Ta, José V. Manjón, Pierrick Coupé, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Adaptive fusion of texture-based grading for Alzheimer’s disease classification. Computer. Med. Imag. Graph. 70 (2018), 8--16.

[56]

Antonio R. Hidalgo-Muñoz, Javier Ramírez, Juan M. Górriz, and Pablo Padilla. 2014. Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI. Front. Aging Neurosci. 6 (2014), 20.

[57]

Chris Hinrichs, Vikas Singh, Lopamudra Mukherjee, Guofan Xu, Moo K. Chung, Sterling C. Johnson, and Alzheimer’s Disease Neuroimaging Initiative. 2009. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48, 1 (2009), 138--149.

[58]

Seyed Hani Hojjati, Ata Ebrahimzadeh, Ali Khazaee, Abbas Babajani-Feremi, and Alzheimer’s Disease Neuroimaging Initiative. 2017. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J. Neurosci. Methods 282 (2017), 69--80.

[59]

Marcia Hon and Naimul Mefraz Khan. 2017. Towards Alzheimer’s disease classification through transfer learning. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM’17). IEEE, 1166--1169.

[60]

Soheil Hor and Mehdi Moradi. 2016. Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features. Med. Image Anal. 34 (2016), 30--41.

[61]

Jean-François Horn, Marie-Odile Habert, Aurélie Kas, Zoulikha Malek, Philippe Maksud, Lucette Lacomblez, Alain Giron, and Bernard Fertil. 2009. Differential automatic diagnosis between Alzheimer’s disease and frontotemporal dementia based on perfusion SPECT images. Artific. Intell. Med. 47, 2 (2009), 147--158.

Digital Library

[62]

Ehsan Hosseini-Asl, Georgy Gimel’farb, and Ayman El-Baz. 2016. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. Arxiv Preprint Arxiv:1607.00556 (2016).

[63]

Chengzhong Huang, Bin Yan, Hua Jiang, and Dahui Wang. 2008. Combining voxel-based morphometry with artificial neural network theory in the application research of diagnosing Alzheimer’s disease. In Proceedings of the International Conference on BioMedical Engineering and Informatics, Vol. 1. IEEE, 250--254.

[64]

Cosimo Ieracitano, Nadia Mammone, Alessia Bramanti, Amir Hussain, and Francesco C. Morabito. 2019. A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing 323 (2019), 96--107.

[65]

I. A. Illán, J. M. Górriz, M. M. López, Javier Ramírez, Diego Salas-Gonzalez, Fermín Segovia, Rosa Chaves, and Carlos García Puntonet. 2011. Computer aided diagnosis of Alzheimer’s disease using component-based SVM. Appl. Soft Comput. 11, 2 (2011), 2376--2382.

Digital Library

[66]

Takeshi Iwatsubo. 2010. Japanese Alzheimer’s disease neuroimaging initiative: Present status and future. Alzheimer’s Dementia 6, 3 (2010), 297--299.

[67]

Clifford R. Jack Jr., Josephine Barnes, Matt A. Bernstein, Bret J. Borowski, James Brewer, Shona Clegg, Anders M. Dale, Owen Carmichael, Christopher Ching, Charles DeCarli, et al. 2015. Magnetic resonance imaging in Alzheimer’s disease neuroimaging initiative 2. Alzheimer’s Dementia 11, 7 (2015), 740--756.

[68]

Rachna Jain, Nikita Jain, Akshay Aggarwal, and D. Jude Hemanth. 2019. Convolutional neural network-based Alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res. (2019).

[69]

Jayadeva, R. Khemchandani, and Suresh Chandra. 2007. Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29, 5 (2007), 905--910.

Digital Library

[70]

Debesh Jha, Ji-In Kim, and Goo-Rak Kwon. 2017. Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network. J. Healthcare Eng. 2017, Article 9060124 (2017).

[71]

Qikun Jiang and Jun Shi. 2014. Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data. In Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 3366--3369.

[72]

Biao Jie, Daoqiang Zhang, Bo Cheng, and Dinggang Shen. 2013. Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer’s disease. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 275--283.

[73]

Biao Jie, Daoqiang Zhang, Chong-Yaw Wee, and Dinggang Shen. 2014. Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum. Brain Map. 35, 7 (2014), 2876--2897.

[74]

Sandhya Joshi, Deepa Shenoy, P. L. Rrashmi, K. R. Venugopal, and L. M. Patnaik. 2010. Classification of Alzheimer’s disease and parkinson’s disease by using machine learning and neural network methods. In Proceedings of the 2nd International Conference on Machine Learning and Computing. IEEE, 218--222.

[75]

Rupali S. Kamathe and Kalyani R. Joshi. 2018. A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease. Biomed. Signal Process. Control 40 (2018), 41--48.

[76]

Subrata Kar and D. Dutta Majumder. 2019. A novel approach of diffusion tensor visualization-based neuro fuzzy classification system for early detection of Alzheimer’s disease. J. Alzheimer’s Dis. Rep. 3, 1 (2019), 1--18.

[77]

K. Kazemi and N. Noorizadeh. 2014. Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation. J. Biomed. Phys. Eng. 4, 1 (2014), 13.

[78]

Ali Khazaee, Ata Ebrahimzadeh, and Abbas Babajani-Feremi. 2016. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging and Behavior 10, 3 (2016), 799--817.

[79]

Laila Khedher, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Abdelbasset Brahim, and Anke Meyer-Baese. 2017. Independent component analysis-support vector machine-based computer-aided diagnosis system for Alzheimer’s with visual support. Int. J. Neural Syst. 27, 03 (2017), 1650050.

[80]

Laila Khedher, Javier Ramírez, Juan Manuel Górriz, Abdelbasset Brahim, and I. A. Illán. 2015. Independent component analysis-based classification of Alzheimer’s disease from segmented MRI data. In Proceedings of the International Work-Conference on the Interplay between Natural and Artificial Computation. Springer, 78--87.

[81]

Jongin Kim and Boreom Lee. 2018. Identification of Alzheimer’s disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine. Hum. Brain Map. 39, 9 (2018), 3728--3741.

[82]

Stefan Klöppel, Cynthia M. Stonnington, Carlton Chu, Bogdan Draganski, Rachael I. Scahill, Jonathan D. Rohrer, Nick C. Fox, Clifford R. Jack Jr., John Ashburner, and Richard S. J. Frackowiak. 2008. Automatic classification of MR scans in Alzheimer’s disease. Brain 131, 3 (2008), 681--689.

[83]

Igor O. Korolev, Laura L. Symonds, Andrea C. Bozoki, and Alzheimer’s Disease Neuroimaging Initiative. 2016. Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PloS One 11, 2 (2016), e0138866.

[84]

V. Krishnakumar, Latha Parthiban, Alzheimer’s Disease Neuroimaging Initiative, et al. 2019. A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer’s disease. J. Neurosci. Methods (2019).

[85]

N. N. Kulkarni and V. K. Bairagi. 2017. Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier. IETE J. Res. 63, 1 (2017), 11--22.

[86]

M. Arun Kumar and Madan Gopal. 2009. Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 36, 4 (2009), 7535--7543.

Digital Library

[87]

Salim Lahmiri and Mounir Boukadoum. 2014. New approach for automatic classification of Alzheimer’s disease, mild cognitive impairment and healthy brain magnetic resonance images. Healthcare Technol. Lett. 1, 1 (2014), 32--36.

[88]

Salim Lahmiri and Amir Shmuel. 2019. Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed. Signal Process. Control 52 (2019), 414--419.

[89]

Ramesh Kumar Lama, Jeonghwan Gwak, Jeong-Seon Park, and Sang-Woong Lee. 2017. Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. J. Healthcare Eng. 2017, Article 5485080 (2017).

[90]

A. V. Lebedev, Eric Westman, G. J. P. Van Westen, M. G. Kramberger, Arvid Lundervold, Dag Aarsland, H. Soininen, I. Kłoszewska, P. Mecocci, M. Tsolaki, et al. 2014. Random forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clin. 6 (2014), 115--125.

[91]

Wook Lee, Byungkyu Park, and Kyungsook Han. 2013. Classification of diffusion tensor images for the early detection of Alzheimer’s disease. Comput. Biol. Med. 43, 10 (2013), 1313--1320.

Digital Library

[92]

Fan Li, Manhua Liu, and Alzheimer’s Disease Neuroimaging Initiative. 2018. Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput. Med. Imag. Graph. 70 (2018), 101--110.

[93]

Han Li, Yashu Liu, Pinghua Gong, Changshui Zhang, Jieping Ye, and Alzheimer’s Disease Neuroimaging Initiative. 2014. Hierarchical interactions model for predicting mild cognitive impairment (MCI) to Alzheimer’s disease (AD) conversion. PloS One 9, 1 (2014), e82450.

[94]

Wei Li, Yifei Zhao, Xi Chen, Yang Xiao, and Yuanyuan Qin. 2018. Detecting Alzheimer’s disease on small dataset: A knowledge transfer perspective. IEEE J. Biomed. Health Info. 23, 3 (2018), 1234--1242.

[95]

Lene Lillemark, Lauge Sørensen, Akshay Pai, Erik B. Dam, and Mads Nielsen. 2014. Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI. BMC Med. Imag. 14, 1 (2014), 21.

[96]

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I. Sánchez. 2017. A survey on deep learning in medical image analysis. Med. Image Anal. 42 (2017), 60--88.

[97]

Feng Liu, Chong-Yaw Wee, Huafu Chen, and Dinggang Shen. 2014. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84 (2014), 466--475.

[98]

Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, and Jianxin Wang. 2018. Classification of Alzheimer’s disease using whole brain hierarchical network. IEEE/ACM Trans. Comput. Biol. Bioinform. 15, 2 (2018), 624--632.

[99]

Jin Liu, Jianxin Wang, Bin Hu, Fang-Xiang Wu, and Yi Pan. 2017. Alzheimer’s disease classification based on individual hierarchical networks constructed with 3D texture features. IEEE Trans. Nanobiosci. 16, 6 (2017), 428--437.

[100]

Manhua Liu, Daoqiang Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2012. Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60, 2 (2012), 1106--1116.

[101]

Mingxia Liu, Jun Zhang, Ehsan Adeli, and Dinggang Shen. 2018. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans. Biomed. Eng. 66, 5 (2018), 1195--1206.

[102]

Siqi Liu, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, Dagan Feng, Michael J. Fulham, et al. 2015. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62, 4 (2015), 1132--1140.

[103]

Xin Liu, Duygu Tosun, Michael W. Weiner, Norbert Schuff, and Alzheimer’s Disease Neuroimaging Initiative. 2013. Locally linear embedding (LLE) for MRI-based Alzheimer’s disease classification. Neuroimage 83 (2013), 148--157.

[104]

Xiaojing Long, Lifang Chen, Chunxiang Jiang, Lijuan Zhang, Alzheimer’s Disease Neuroimaging Initiative, et al. 2017. Prediction and classification of Alzheimer’s disease based on quantification of MRI deformation. PloS One 12, 3 (2017), e0173372.

[105]

Xiaojing Long and Chris Wyatt. 2010. An automatic unsupervised classification of MR images in Alzheimer’s disease. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2910--2917.

[106]

M. López, J. Ramírez, J. M. Górriz, D. Salas-Gonzalez, I. Alvarez, F. Segovia, and C. G. Puntonet. 2009. Automatic tool for Alzheimer’s disease diagnosis using PCA and bayesian classification rules. Electron. Lett. 45, 8 (2009), 389--391.

[107]

Donghuan Lu, Karteek Popuri, Gavin Weiguang Ding, Rakesh Balachandar, Mirza Faisal Beg, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Multiscale deep neural network-based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med. Image Anal. 46 (2018), 26--34.

[108]

Shen Lu, Yong Xia, Weidong Cai, Michael Fulham, David Dagan Feng, Alzheimer’s Disease Neuroimaging Initiative, et al. 2017. Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging. Comput. Med. Imag. Graph. 60 (2017), 35--41.

[109]

Benoît Magnin, Lilia Mesrob, Serge Kinkingnéhun, Mélanie Pélégrini-Issac, Olivier Colliot, Marie Sarazin, Bruno Dubois, Stéphane Lehéricy, and Habib Benali. 2009. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51, 2 (2009), 73--83.

[110]

Belathur Suresh Mahanand, Sundaram Suresh, Narasimhan Sundararajan, and M. Aswatha Kumar. 2012. Identification of brain regions responsible for Alzheimer’s disease using a Self-adaptive resource allocation network. Neural Netw. 32 (2012), 313--322.

Digital Library

[111]

Rigel Mahmood and Bishad Ghimire. 2013. Automatic detection and classification of Alzheimer’s disease from MRI scans using principal component analysis and artificial neural networks. In Proceedings of the 20th International Conference on Systems, Signals and Image Processing (IWSSIP’13). IEEE, 133--137.

[112]

José María Mateos-Pérez, Mahsa Dadar, María Lacalle-Aurioles, Yasser Iturria-Medina, Yashar Zeighami, and Alan C. Evans. 2018. Structural neuroimaging as clinical predictor: A review of machine learning applications. NeuroImage: Clin. 20 (2018), 506--522.

[113]

Ali Mazaheri, Katrien Segaert, John Olichney, Jin-Chen Yang, Yu-Qiong Niu, Kim Shapiro, and Howard Bowman. 2018. EEG oscillations during word processing predict MCI conversion to Alzheimer’s disease. NeuroImage: Clin. 17 (2018), 188--197.

[114]

Linda K. McEvoy, Christine Fennema-Notestine, J. Cooper Roddey, Donald J. Hagler Jr., Dominic Holland, David S. Karow, Christopher J. Pung, James B. Brewer, and Anders M. Dale. 2009. Alzheimer disease: Quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251, 1 (2009), 195--205.

[115]

Lilia Mesrob, Benoit Magnin, Olivier Colliot, Marie Sarazin, Valérie Hahn-Barma, Bruno Dubois, Patrick Gallinari, Stéphane Lehéricy, Serge Kinkingnéhun, and Habib Benali. 2008. Identification of atrophy patterns in Alzheimer’s disease based on SVM feature selection and anatomical parcellation. In Proceedings of the International Workshop on Medical Imaging and Virtual Reality. Springer, 124--132.

Digital Library

[116]

Rui Min, Guorong Wu, Jian Cheng, Qian Wang, Dinggang Shen, and Alzheimer’s Disease Neuroimaging Initiative. 2014. Multi-atlas-based representations for Alzheimer’s disease diagnosis. Hum. Brain Map. 35, 10 (2014), 5052--5070.

[117]

Christiane Möller, Yolande A. L. Pijnenburg, Wiesje M. van der Flier, Adriaan Versteeg, Betty Tijms, Jan C. de Munck, Anne Hafkemeijer, Serge A. R. B. Rombouts, Jeroen van der Grond, John van Swieten, et al. 2015. Alzheimer disease and behavioral variant frontotemporal dementia: Automatic classification based on cortical atrophy for single-subject diagnosis. Radiology 279, 3 (2015), 838--848.

[118]

Elaheh Moradi, Antonietta Pepe, Christian Gaser, Heikki Huttunen, Jussi Tohka, and Alzheimer’s Disease Neuroimaging Initiative. 2015. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage 104 (2015), 398--412.

[119]

Laurence O’Dwyer, Franck Lamberton, Arun L. W. Bokde, Michael Ewers, Yetunde O. Faluyi, Colby Tanner, Bernard Mazoyer, Desmond O’Neill, Máiréad Bartley, D. Rónán Collins, et al. 2012. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PloS One 7, 2 (2012), e32441.

[120]

Andrés Ortiz, Juan M. Górriz, Javier Ramírez, Francisco Jesús Martínez-Murcia, and Alzheimer’s Disease Neuroimaging Initiative. 2013. 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.

Digital Library

[121]

Andrés Ortiz, Juan M. Górriz, Javier Ramírez, Francisco J. Martinez-Murcia, and Alzheimer’s Disease Neuroimaging Initiative. 2014. Automatic ROI selection in structural brain MRI using SOM 3D projection. PloS One 9, 4 (2014), e93851.

[122]

Andrés Ortiz, Jorge Munilla, Ignacio Álvarez-Illán, Juan M. Górriz, Javier Ramírez, and Alzheimer’s Disease Neuroimaging Initiative. 2015. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer’s disease diagnosis. Front. Comput. Neurosci. 9 (2015), 132.

[123]

Andres Ortiz, Jorge Munilla, Juan M. Gorriz, and Javier Ramirez. 2016. Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int. J. Neural Syst. 26, 7 (2016), 1650025.

[124]

P. Padilla, J. M. Górriz, J. Ramírez, E. W. Lang, R. Chaves, F. Segovia, M. López, D. Salas-González, and I. Álvarez. 2010. Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease based on NMF for feature extraction. Neurosci. Lett. 479, 3 (2010), 192--196.

[125]

Maria Paraskevaidi, Camilo L. M. Morais, Diane E. Halliwell, David M. A. Mann, David Allsop, Pierre L. Martin-Hirsch, and Francis L. Martin. 2018. Raman spectroscopy to diagnose Alzheimer’s disease and dementia with lewy bodies in blood. ACS Chem. Neurosci. 9, 11 (2018), 2786--2794.

[126]

Christina Patterson. 2018. The state of the art of dementia research: New frontiers. World Alzheimer’s Report 2018 (2018).

[127]

Adrien Payan and Giovanni Montana. 2015. Predicting Alzheimer’s disease: A neuroimaging study with 3D convolutional neural networks. Arxiv Preprint Arxiv:1502.02506 (2015).

[128]

Enrico Pellegrini, Lucia Ballerini, Maria Del C. Valdes Hernandez, Francesca M. Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz-Maniega, Dominic Job, Cyril Pernet, et al. 2018. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer’s Dementia: Diagn. Assess. Dis. Monitor. 10 (2018), 519--535.

[129]

Jialin Peng, Xiaofeng Zhu, Ye Wang, Le An, and Dinggang Shen. 2019. Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis. Pattern Recogn. 88 (2019), 370--382.

Digital Library

[130]

A. A. Petrosian, D. V. Prokhorov, W. Lajara-Nanson, and R. B. Schiffer. 2001. Recurrent neural network-based approach for early recognition of Alzheimer’s disease in EEG. Clin. Neurophysiol. 112, 8 (2001), 1378--1387.

[131]

Claudia Plant, Stefan J. Teipel, Annahita Oswald, Christian Böhm, Thomas Meindl, Janaina Mourao-Miranda, Arun W. Bokde, Harald Hampel, and Michael Ewers. 2010. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage 50, 1 (2010), 162--174.

[132]

Maciej Plocharski, Lasse Riis Østergaard, Alzheimer’s Disease Neuroimaging Initiative, et al. 2016. Extraction of sulcal medial surface and classification of Alzheimer’s disease using sulcal features. Comput. Methods Programs Biomed. 133 (2016), 35--44.

Digital Library

[133]

María Quintana, Joan Guàrdia, Gonzalo Sánchez-Benavides, Miguel Aguilar, José Luis Molinuevo, Alfredo Robles, María Sagrario Barquero, Carmen Antúnez, Carlos Martínez-Parra, Anna Frank-García, et al. 2012. Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer’s disease. J. Clin. Exper. Neuropsychol. 34, 2 (2012), 195--208.

[134]

Javier Ramírez, J. M. Górriz, Diego Salas-Gonzalez, A. Romero, Míriam López, Ignacio Álvarez, and Manuel Gómez-Río. 2013. Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Info. Sci. 237 (2013), 59--72.

[135]

J. Ramírez, J. M. Górriz, F. Segovia, R. Chaves, D. Salas-Gonzalez, M. López, I. Álvarez, and P. Padilla. 2010. Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neurosci. Lett. 472, 2 (2010), 99--103.

[136]

Javier Ramírez, Juan Manuel Górriz, Míriam López, Diego Salas-Gonzalez, Ignacio Álvarez, Fermín Segovia, and Carlos García Puntonet. 2008. Early detection of the Alzheimer’s disease combining feature selection and kernel machines. In Proceedings of the International Conference on Neural Information Processing. Springer, 410--417.

[137]

Anil Rao, Ying Lee, Achim Gass, and Andreas Monsch. 2011. Classification of Alzheimer’s disease from structural MRI using sparse logistic regression with optional spatial regularization. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 4499--4502.

[138]

Saima Rathore, Mohamad Habes, Muhammad Aksam Iftikhar, Amanda Shacklett, and Christos Davatzikos. 2017. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage 155 (2017), 530--548.

[139]

Alessandra Retico, Paolo Bosco, Piergiorgio Cerello, Elisa Fiorina, Andrea Chincarini, and Maria Evelina Fantacci. 2015. Predictive models based on support vector machines: Whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease. J. Neuroimag. 25, 4 (2015), 552--563.

[140]

B. Richhariya and Muhammad Tanveer. 2018. EEG signal classification using universum support vector machine. Expert Syst. Appl. 106 (2018), 169--182.

[141]

Bharat Richhariya and Muhammad Tanveer. 2018. A robust fuzzy least squares twin support vector machine for class imbalance learning. Appl. Soft Comput. 71 (2018), 418--432.

Digital Library

[142]

Pedro Rodrigues and João Paulo Teixeira. 2011. Artificial neural networks in the discrimination of Alzheimer’s disease. In Proceedings of the International Conference on ENTERprise Information Systems. Springer, 272--281.

[143]

Diego Salas-Gonzalez, Juan Manuel Górriz, Javier Ramírez, Míriam López, Ignacio Álvarez, Fermín Segovia, and Carlos García Puntonet. 2008. Computer aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. In Proceedings of the International Conference on Neural Information Processing. Springer, 418--425.

[144]

Ziad Sankari and Hojjat Adeli. 2011. Probabilistic neural networks for diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J. Neurosci. Methods 197, 1 (2011), 165--170.

[145]

Saman Sarraf, Danielle D. DeSouza, John Anderson, Ghassem Tofighi, et al. 2017. DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv (2017), 070441.

[146]

Alexandre Savio, Maite García-Sebastián, Carmen Hernández, Manuel Graña, and Jorge Villanúa. 2009. Classification results of artificial neural networks for Alzheimer’s disease detection. In Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. Springer, 641--648.

[147]

Daniel Schmitter, Alexis Roche, Bénédicte Maréchal, Delphine Ribes, Ahmed Abdulkadir, Meritxell Bach-Cuadra, Alessandro Daducci, Cristina Granziera, Stefan Klöppel, Philippe Maeder, et al. 2015. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroImage: Clin. 7 (2015), 7--17.

[148]

Tijn M. Schouten, Marisa Koini, Frank de Vos, Stephan Seiler, Jeroen van der Grond, Anita Lechner, Anne Hafkemeijer, Christiane Möller, Reinhold Schmidt, Mark de Rooij, et al. 2016. Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer’s disease. NeuroImage: Clin. 11 (2016), 46--51.

[149]

Fermín Segovia, J. M. Górriz, Javier Ramírez, Diego Salas-Gonzalez, and Ignacio Álvarez. 2013. Early diagnosis of Alzheimer’s disease based on partial least squares and support vector machine. Expert Syst. Appl. 40, 2 (2013), 677--683.

Digital Library

[150]

F. Segovia, J. M. Górriz, J. Ramírez, D. Salas-González, I. Álvarez, M. López, R. Chaves, and P. Padilla. 2010. Classification of functional brain images using a GMM-based multi-variate approach. Neurosci. Lett. 474, 1 (2010), 58--62.

[151]

Dinggang Shen, Guorong Wu, and Heung-Il Suk. 2017. Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19 (2017), 221--248.

[152]

Jinhua Sheng, Bocheng Wang, Qiao Zhang, Qingqiang Liu, Yangjie Ma, Weixiang Liu, Meiling Shao, and Bin Chen. 2019. A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients. Behav. Brain Res. 365 (2019), 210--221.

[153]

Jun Shi, Xiao Zheng, Yan Li, Qi Zhang, and Shihui Ying. 2018. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J. Biomed. Health Info. 22, 1 (2018), 173--183.

[154]

Zhenghao Shi, Lifeng He, Kenji Suzuki, Tsuyoshi Nakamura, and Hidenori Itoh. 2009. Survey on neural networks used for medical image processing. Int. J. Comput. Sci. 3, 1 (2009), 86.

[155]

Nikhil Singh, P. Thomas Fletcher, J. Samuel Preston, Richard D. King, J. S. Marron, Michael W. Weiner, Sarang Joshi, and Alzheimer’s Disease Neuroimaging Initiative (ADNI). 2014. Quantifying anatomical shape variations in neurological disorders. Med. Image Anal. 18, 3 (2014), 616--633.

[156]

Simeon Spasov, Luca Passamonti, Andrea Duggento, Pietro Lio, Nicola Toschi, Alzheimer’s Disease Neuroimaging Initiative, et al. 2019. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. NeuroImage 189 (2019), 276--287.

[157]

Gabriela Spulber, Andrew Simmons, J.-S. Muehlboeck, Patrizia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka Soininen, Christian Spenger, Simon Lovestone, et al. 2013. An MRI-based index to measure the severity of Alzheimer’s disease-like structural pattern in subjects with mild cognitive impairment. J. Internal Med. 273, 4 (2013), 396--409.

[158]

Jonathan Stoeckel and Glenn Fung. 2005. SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05). IEEE, 8--pp.

Digital Library

[159]

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, and Alzheimer’s Disease Neuroimaging Initiative. 2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101 (2014), 569--582.

[160]

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, and Alzheimer’s Disease Neuroimaging Initiative. 2015. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220, 2 (2015), 841--859.

[161]

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, and Alzheimer’s Disease Neuroimaging Initiative. 2017. Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37 (2017), 101--113.

[162]

Heung-Il Suk and Dinggang Shen. 2013. Deep learning-based feature representation for AD/MCI classification. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 583--590.

[163]

Heung-Il Suk and Dinggang Shen. 2014. Subclass-based multi-task learning for Alzheimer’s disease diagnosis. Front. Aging Neurosci. 6 (2014), 168.

[164]

Heung-Il Suk and Dinggang Shen. 2016. Deep ensemble sparse regression network for Alzheimer’s disease diagnosis. In Proceedings of the International Workshop on Machine Learning in Medical Imaging. Springer, 113--121.

[165]

Heung-Il Suk, Chong-Yaw Wee, Seong-Whan Lee, and Dinggang Shen. 2016. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129 (2016), 292--307.

[166]

Zhuo Sun, Yuchuan Qiao, Boudewijn P. F. Lelieveldt, Marius Staring, Alzheimer’s Disease NeuroImaging Initiative, et al. 2018. Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer’s disease classification. NeuroImage 178 (2018), 445--460.

[167]

Sabina Tangaro, Annarita Fanizzi, Nicola Amoroso, Roberto Bellotti, Alzheimer’s Disease Neuroimaging Initiative, et al. 2017. A fuzzy-based system reveals Alzheimer’s disease onset in subjects with mild cognitive impairment. Physica Medica 38 (2017), 36--44.

[168]

Mohammad Tanveer, Mohammad Asif Khan, and Shen-Shyang Ho. 2016. Robust energy-based least squares twin support vector machines. Appl. Intell. 45, 1 (2016), 174--186.

Digital Library

[169]

M. Termenon and Manuel Graña. 2012. A two stage sequential ensemble applied to the classification of Alzheimer’s disease based on mri features. Neural Process. Lett. 35, 1 (2012), 1--12.

Digital Library

[170]

M. Termenon, Manuel Grana, A. Besga, J. Echeveste, and A. Gonzalez-Pinto. 2013. Lattice independent component analysis feature selection on diffusion weighted imaging for Alzheimer’s disease classification. Neurocomputing 114 (2013), 132--141.

[171]

Yingjie Tian and Zhiquan Qi. 2014. Review on: Twin support vector machines. Ann. Data Sci. 1, 2 (2014), 253--277.

[172]

Tong Tong, Katherine Gray, Qinquan Gao, Liang Chen, Daniel Rueckert, Alzheimer’s Disease Neuroimaging Initiative, et al. 2017. Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion. Pattern Recogn. 63 (2017), 171--181.

Digital Library

[173]

Rick van Veen, L. Talavera Martinez, R. V. Kogan, S. K. Meles, Deborah Mudali, Jos B. T. M. Roerdink, F. Massa, M. Grazzini, Jose A. Obeso, Maria C. Rodriguez-Oroz, et al. 2018. Machine learning based analysis of FDG-PET image data for the diagnosis of neurodegenerative diseases. In Proceedings of the International Conference on Applications of Intelligent Systems (APPIS’18). 280--289.

[174]

Dallas P. Veitch, Michael W. Weiner, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Robert C. Green, Danielle Harvey, Clifford R. Jack Jr., William Jagust, John C. Morris, et al. 2018. Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s disease neuroimaging initiative. Alzheimer’s Dementia 15, 1 (2019), 106--152.

[175]

Prashanthi Vemuri, Jeffrey L. Gunter, Matthew L. Senjem, Jennifer L. Whitwell, Kejal Kantarci, David S. Knopman, Bradley F. Boeve, Ronald C. Petersen, and Clifford R. Jack Jr. 2008. Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies. Neuroimage 39, 3 (2008), 1186--1197.

[176]

Prashanthi Vemuri and Clifford R. Jack. 2010. Role of structural MRI in Alzheimer’s disease. Alzheimer’s Res. Ther. 2, 4 (2010), 23.

[177]

Hongfei Wang, Yanyan Shen, Shuqiang Wang, Tengfei Xiao, Liming Deng, Xiangyu Wang, and Xinyan Zhao. 2019. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and alzheimer’s disease. Neurocomputing 333 (2019), 145--156.

Digital Library

[178]

Kun Wang, Meng Liang, Liang Wang, Lixia Tian, Xinqing Zhang, Kuncheng Li, and Tianzi Jiang. 2007. Altered functional connectivity in early alzheimer’s disease: A resting-state fMRI study. Hum. Brain Map. 28, 10 (2007), 967--978.

[179]

Shuihua Wang, Yudong Zhang, Zhengchao Dong, Sidan Du, Genlin Ji, Jie Yan, Jiquan Yang, Qiong Wang, Chunmei Feng, and Preetha Phillips. 2015. Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int. J. Imag. Syst. Technol. 25, 2 (2015), 153--164.

Digital Library

[180]

Tingyan Wang, Robin G. Qiu, and Ming Yu. 2018. Predictive modeling of the progression of Alzheimer’s disease with recurrent neural networks. Sci. Rep. 8 (2018).

[181]

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Robert C. Green, Danielle Harvey, Clifford R. Jack Jr., William Jagust, John C. Morris, et al. 2017. Recent publications from the alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimer’s Dementia 13, 4 (2017), e1--e85.

[182]

Eric Westman, Andrew Simmons, J.-Sebastian Muehlboeck, Patrizia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka Soininen, Michael W. Weiner, Simon Lovestone, et al. 2011. AddNeuroMed and ADNI: Similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage 58, 3 (2011), 818--828.

[183]

Yitian Xu, Xianli Pan, Zhijian Zhou, Zhiji Yang, and Yuqun Zhang. 2015. Structural least square twin support vector machine for classification. Appl. Intell. 42, 3 (2015), 527--536.

Digital Library

[184]

Shih-Ting Yang, Jiann-Der Lee, Tzyh-Chyang Chang, Chung-Hsien Huang, Jiun-Jie Wang, Wen-Chuin Hsu, Hsiao-Lung Chan, Yau-Yau Wai, and Kuan-Yi Li. 2013. Discrimination between Alzheimer’s disease and mild cognitive impairment using SOM and PSO-SVM. Comput. Math. Methods Med. 2013, Article 253670 (2013).

[185]

Shih-Ting Yang, Jiann-Der Lee, Chung-Hsien Huang, Jiun-Jie Wang, Wen-Chuin Hsu, and Yau-Yau Wai. 2010. Computer-aided diagnosis of alzheimer’s disease using multiple features with artificial neural network. In Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Springer, 699--705.

[186]

Jieping Ye, Kewei Chen, Teresa Wu, Jing Li, Zheng Zhao, Rinkal Patel, Min Bae, Ravi Janardan, Huan Liu, Gene Alexander, et al. 2008. Heterogeneous data fusion for alzheimer’s disease study. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1025--1033.

Digital Library

[187]

Nianyin Zeng, Hong Qiu, Zidong Wang, Weibo Liu, Hong Zhang, and Yurong Li. 2018. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 320 (2018), 195--202.

[188]

Daoqiang Zhang and Dinggang Shen. 2011. Semi-supervised multimodal classification of alzheimer’s disease. In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 1628--1631.

[189]

Daqing Zhang, Jianfeng Xiao, Nannan Zhou, Mingyue Zheng, Xiaomin Luo, Hualiang Jiang, and Kaixian Chen. 2015. A genetic algorithm-based support vector machine model for blood-brain barrier penetration prediction. BioMed Res. Int. 2015, Article 292683 (2015).

[190]

Jie Zhang, Cynthia Stonnington, Qingyang Li, Jie Shi, Robert J. Bauer, Boris A. Gutman, Kewei Chen, Eric M. Reiman, Paul M. Thompson, Jieping Ye, et al. 2016. Applying sparse coding to surface multivariate tensor-based morphometry to predict future cognitive decline. In Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI’16). IEEE, 646--650.

[191]

Yudong Zhang, Zhengchao Dong, Preetha Phillips, Shuihua Wang, Genlin Ji, Jiquan Yang, and Ti-Fei Yuan. 2015. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 9 (2015), 66.

[192]

Yudong Zhang and Shuihua Wang. 2015. Detection of Alzheimer’s disease by displacement field and machine learning. Peer J. 3 (2015), e1251.

[193]

Yudong Zhang, Shuihua Wang, Preetha Phillips, Zhengchao Dong, Genlin Ji, and Jiquan Yang. 2015. Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed. Signal Process. Control 21 (2015), 58--73.

[194]

Ying-Teng Zhang and Shen-Quan Liu. 2018. Individual identification using multi-metric of DTI in Alzheimer’s disease and mild cognitive impairment. Chinese Phys. B 27, 8 (2018), 088702.

[195]

Weihao Zheng, Zhijun Yao, Yuanwei Xie, Jin Fan, and Bin Hu. 2018. Identification of Alzheimer’s disease and mild cognitive impairment using networks constructed based on multiple morphological brain features. Biol. Psych.: Cogn. Neurosci. Neuroimag. 3, 10 (2018), 887--897.

[196]

Xiao Zheng, Jun Shi, Yan Li, Xiao Liu, and Qi Zhang. 2016. Multi-modality stacked deep polynomial network-based feature learning for Alzheimer’s disease diagnosis. In Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI’16). IEEE, 851--854.

[197]

Xiao Zheng, Jun Shi, Qi Zhang, Shihui Ying, and Yan Li. 2017. Improving MRI-based diagnosis of Alzheimer’s disease via an ensemble privileged information learning algorithm. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI’17). IEEE, 456--459.

[198]

Ke Zhou, Wenguang He, Yonghui Xu, Gangqiang Xiong, and Jie Cai. 2018. Feature selection and transfer learning for Alzheimer’s disease clinical diagnosis. Appl. Sci. 8, 8 (2018), 1372.

[199]

Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, and Dinggang Shen. 2016. Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63, 3 (2016), 607--618.

[200]

Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Dinggang Shen, and Guorong Wu. 2016. Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 264--272.

Cited By

Fah HBohn LGreiner RDixon R(2025)Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer’s disease spectrum: demonstration analyses in the COMPASS-ND studyFrontiers in Aging Neuroscience10.3389/fnagi.2025.154251417Online publication date: 4-Mar-2025
https://doi.org/10.3389/fnagi.2025.1542514
Hassan NMiah ASuzuki KOkuyama YShin J(2025)Stacked CNN-based multichannel attention networks for Alzheimer disease detectionScientific Reports10.1038/s41598-025-85703-x15:1Online publication date: 17-Feb-2025
https://doi.org/10.1038/s41598-025-85703-x
Peng WMa YLi CDai WFu XLiu LLiu LLiu J(2025)Fusion of brain imaging genetic data for alzheimer’s disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networksNeural Networks10.1016/j.neunet.2024.107020184:COnline publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.107020
Show More Cited By

Index Terms

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
    2. Machine learning approaches
      1. Neural networks
  2. Parallel computing methodologies
    1. Parallel algorithms
      1. Vector / streaming algorithms

Recommendations

Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods
IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing

Alzheimer's Disease is a gradual, irreversible brain disease that deteriorates a patient's memory, cognitive functions and shrinks the brain's size, eventually leading to death. Based on recent research, it is found that AD is the third leading cause of ...
On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier
Abstract
Alzheimer’s disease (AD) is considered to be one of the most frequent neurogenerative dementia in the elderly population. In order to improve the quality of life span, early detection of AD has drawn significant attention to the researchers ...
Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment

A multi-modal discriminant DL algorithm for AD/MCI classification is proposed.The mSCDDL could enhance the recognition rate compared with some other algorithms.A weighted combination framework for multi-feature fusion is adopted into the DL scheme. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 1s

Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging

January 2020

376 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3388236

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2020

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 May 2019

Published in TOMM Volume 16, Issue 1s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Council of Scientific 8 Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme
Department of Science and Technology, INDIA as Ramanujan fellowship

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

206
Total Citations
View Citations
5,619
Total Downloads

Downloads (Last 12 months)765
Downloads (Last 6 weeks)87

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Fah HBohn LGreiner RDixon R(2025)Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer’s disease spectrum: demonstration analyses in the COMPASS-ND studyFrontiers in Aging Neuroscience10.3389/fnagi.2025.154251417Online publication date: 4-Mar-2025
https://doi.org/10.3389/fnagi.2025.1542514
Hassan NMiah ASuzuki KOkuyama YShin J(2025)Stacked CNN-based multichannel attention networks for Alzheimer disease detectionScientific Reports10.1038/s41598-025-85703-x15:1Online publication date: 17-Feb-2025
https://doi.org/10.1038/s41598-025-85703-x
Peng WMa YLi CDai WFu XLiu LLiu LLiu J(2025)Fusion of brain imaging genetic data for alzheimer’s disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networksNeural Networks10.1016/j.neunet.2024.107020184:COnline publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.107020
Huang YLiang XXie JLu XMiao XLiu WZhang FKang GMa LTang SZhan J(2025)OpenClinicalAIExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125528261:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125528
Kaur ISachdeva R(2025)Prediction Models for Early Detection of Alzheimer: Recent Trends and Future ProspectsArchives of Computational Methods in Engineering10.1007/s11831-025-10246-3Online publication date: 1-Mar-2025
https://doi.org/10.1007/s11831-025-10246-3
Singh SChaturvedi A(2025)Leveraging Handwriting Dynamics, Explainable AI and Machine Learning for Alzheimer PredictionComputational Intelligence in Communications and Business Analytics10.1007/978-3-031-81342-9_27(338-351)Online publication date: 12-Feb-2025
https://doi.org/10.1007/978-3-031-81342-9_27
Sriharsha A(2025)Using Deep Learning to Classify and Diagnose Alzheimer's DiseaseArtificial Intelligence and Cybersecurity in Healthcare10.1002/9781394229826.ch19(413-428)Online publication date: 20-Feb-2025
https://doi.org/10.1002/9781394229826.ch19
Henríquez PAraya N(2024)Multimodal Alzheimer’s disease classification through ensemble deep random vector functional link neural networkPeerJ Computer Science10.7717/peerj-cs.259010(e2590)Online publication date: 13-Dec-2024
https://doi.org/10.7717/peerj-cs.2590
Al-Rawashdeh HUsman ADutta AWahab Sait A(2024)Ensemble Learning-based Alzheimer’s Disease Diagnosis Using Magnetic Resonance ImagingJournal of Disability Research10.57197/JDR-2024-00673:6Online publication date: 2024
https://doi.org/10.57197/JDR-2024-0067
Mathews EJabez J(2024)Classifying alzheimer's disease from sMRI data using a hybrid deep learning approachesSalud, Ciencia y Tecnología - Serie de Conferencias10.56294/sctconf20246513(651)Online publication date: 7-Mar-2024
https://doi.org/10.56294/sctconf2024651
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Issue’s Table of Contents