Alzheimer Disease Detection Techniques and Methods: A Review.
DOI:
https://doi.org/10.9781/ijimai.2021.04.005Keywords:
Alzheimer's Disease, Literature Review, Mild Cognitive Impairment, Neuroimaging, Machine Learning, Classification, Deep LearningAbstract
Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper.
Downloads
References
M. A. Binnewijzend, M. M. Schoonheim, E. Sanz-Arigita, A. M. Wink, W. M. van der Flier, N. Tolboom, et al., “Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment,” Neurobiology of aging, vol. 33, pp. 2018-2028, 2012. https://doi.org/10.1016/j.neurobiolaging.2011.07.003
H. Braak and E. Braak, “Neuropathological stageing of Alzheimer-related changes,” Acta neuropathologica, vol. 82, pp. 239-259, 1991. https://doi.org/10.1007/BF00308809
E. E. Bron, M. Smits, W. M. Van Der Flier, H. Vrenken, F. Barkhof, P. Scheltens, et al., “Standardized evaluation of algorithms for computeraided diagnosis of dementia based on structural MRI: the CADDementia challenge,” NeuroImage, vol. 111, pp. 562-579, 2015 https://doi.org/10.1016/j.neuroimage.2015.01.048
V. Camus, P. Payoux, L. Barré, B. Desgranges, T. Voisin, C. Tauber, et al., “Using PET with 18 F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment,” European journal of nuclear medicine and molecular imaging, vol. 39, pp. 621-631, 2012. https://doi.org/10.1007/s00259-011-2021-8
R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. G. Tangalos, and E. Kokmen, “Mild cognitive impairment: clinical characterization and outcome,” Archives of neurology, vol. 56, pp. 303-308, 1999. doi: 10.1001/archneur.56.3.303.
A. s. Disease and R. D. Association, 2017 Alzheimer’s Disease Facts and Figures: Includes a Special Report on the Next Frontier of Alzheimer’s Research: Alzheimer’s Association, 2017. https://doi.org/10.1016/j.jalz.2017.02.006
J. P. Lerch, J. Pruessner, A. P. Zijdenbos, D. L. Collins, S. J. Teipel, H. Hampel, et al., “Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls,” Neurobiology of aging, vol. 29, pp. 23-30, 2008. https://doi.org/10.1016/j.neurobiolaging.2006.09.013
S. Klöppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, et al., “Automatic classification of MR scans in Alzheimer’s disease,” Brain, vol. 131, pp. 681-689, 2008. https://doi.org/10.1093/brain/awm319
R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, “Forecasting the global burden of Alzheimer’s disease,” Alzheimer’s & dementia, vol. 3, pp. 186-191, 2007. https://doi.org/10.1016/j.jalz.2007.04.381
R. L. Buckner, “Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate,” Neuron, vol. 44, pp. 195-208, 2004. https://doi.org/10.1016/j.neuron.2004.09.006
G. F. Busatto, G. E. Garrido, O. P. Almeida, C. C. Castro, C. H. Camargo, C. G. Cid, et al., “A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease,” Neurobiology of aging, vol. 24, pp. 221-231, 2003. https://doi.org/10.1016/S0197-4580(02)00084-2
C. Cabral, P. M. Morgado, D. C. Costa, M. Silveira, and A. s. D. N. Initiative, “Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages,” Computers in biology and medicine, vol. 58, pp. 101-109, 2015. https://doi.org/10.1016/j.compbiomed.2015.01.003
H. Wang, S. Yan, D. Xu, X. Tang, and T. Huang, “Trace ratio vs. ratio trace for dimensionality reduction,” in Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, 2007, pp. 1-8. DOI: 10.1109/CVPR.2007.382983.
M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nature reviews neuroscience, vol. 8, p. 700, 2007. https://doi.org/10.1038/nrn2201
M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, “Functional connectivity in the resting brain: a network analysis of the default mode hypothesis,” Proceedings of the National Academy of Sciences, vol. 100, pp. 253-258, 2003. https://doi.org/10.1073/pnas.0135058100
P. T. Fox, A. R. Laird, and J. L. Lancaster, “Coordinate‐based voxel‐wise meta‐analysis: Dividends of spatial normalization. Report of a virtual workshop,” Human brain mapping, vol. 25, pp. 1-5, 2005. DOI: 10.1002/hbm.20139.
Y. He, L. Wang, Y. Zang, L. Tian, X. Zhang, K. Li, et al., “Regional coherence changes in the early stages of Alzheimer’s disease: a combined structural and resting-state functional MRI study,” Neuroimage, vol. 35, pp. 488-500, 2007. https://doi.org/10.1016/j.neuroimage.2006.11.042
S. Teipel, A. Drzezga, M. J. Grothe, H. Barthel, G. Chételat, N. Schuff, et al., “Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection,” The Lancet Neurology, vol. 14, pp. 1037-1053, 2015. https://doi.org/10.1016/S1474-4422(15)00093-9
Y. Chen, D. Wolk, J. Reddin, M. Korczykowski, P. Martinez, E. Musiek, et al., “Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease,” Neurology, p. WNL. 0b013e31823a0ef7, 2011. DOI: https://doi.org/10.1212/WNL.0b013e31823a0ef7
B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, et al., “Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI,” Neuroradiology, vol. 51, pp. 73-83, 2009. DOI: https://doi.org/10.1007/s00234-008-0463-x
C. Plant, S. J. Teipel, A. Oswald, C. Böhm, T. Meindl, J. Mourao-Miranda, et al., “Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease,” Neuroimage, vol. 50, pp. 162-174, 2010. https://doi.org/10.1016/j.neuroimage.2009.11.046
J. Friedrich, R. Urbanczik, and W. Senn, “Code-specific learning rules improve action selection by populations of spiking neurons,” International journal of neural systems, vol. 24, p. 1450002, 2014. https://doi.org/10.1142/S0129065714500026
G. Lee, M. Kwon, S. Kavuri, and M. Lee, “Action-perception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals,” Integrated Computer-Aided Engineering, vol. 21, pp. 295-310, 2014. DOI: 10.3233/ICA-140464.
U. S. Shanthamallu, A. Spanias, C. Tepedelenlioglu, and M. Stanley, “A brief survey of machine learning methods and their sensor and IoT applications,” in Information, Intelligence, Systems & Applications (IISA), 2017 8th International Conference on, 2017, pp. 1-8. DOI: 10.1109/IISA.2017.8316459.
F. L. Seixas, B. Zadrozny, J. Laks, A. Conci, and D. C. M. Saade, “A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳ s disease and mild cognitive impairment,” Computers in biology and medicine, vol. 51, pp. 140-158, 2014. https://doi.org/10.1016/j.compbiomed.2014.04.010
S. Liu, Y. Song, W. Cai, S. Pujol, R. Kikinis, X. Wang, et al., “Multifold Bayesian kernelization in Alzheimer’s diagnosis,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013, pp. 303-310. DOI: https://doi.org/10.1007/978-3-642-40763-5_38
M. Catá Villá, “Feature selection methods for predicting pre-clinical stage in Alzheirmer’s Disease,” Universitat Politècnica de Catalunya, 2014.
M. López, J. Ramírez, J. Górriz, D. Salas-Gonzalez, I. Alvarez, F. Segovia, et al., “Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules,” Electronics Letters, vol. 45, pp. 389-391, 2009. DOI: 10.1049/el.2009.0176.
F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: a tutorial overview,” Neuroimage, vol. 45, pp. S199-S209, 2009. https://doi.org/10.1016/j.neuroimage.2008.11.007
V. Vapnik, The nature of statistical learning theory: Springer science & business media, 2013.
G. Mirzaei, A. Adeli, and H. Adeli, “Imaging and machine learning techniques for diagnosis of Alzheimer’s disease,” Reviews in the Neurosciences, vol. 27, pp. 857-870, 2016. DOI: https://doi.org/10.1515/revneuro-2016-0029
H. Furuta, K. Maeda, and E. Watanabe, “Application of genetic algorithm to aesthetic design of bridge structures,” Computer‐Aided Civil and Infrastructure Engineering, vol. 10, pp. 415-421, 1995. https://doi.org/10.1111/j.1467-8667.1995.tb00301.x
J. S. Chou and A. D. Pham, “Smart artificial firefly colony algorithm‐based support vector regression for enhanced forecasting in civil engineering,” Computer‐Aided Civil and Infrastructure Engineering, vol. 30, pp. 715-732, 2015.
H. Adeli, Advances in design optimization: CRC press, 1994.
O. Chapelle, V. Sindhwani, and S. S. Keerthi, “Optimization techniques for semi-supervised support vector machines,” Journal of Machine Learning Research, vol. 9, pp. 203-233, 2008.
V. Vural and J. G. Dy, “A hierarchical method for multi-class support vector machines,” in Proceedings of the twenty-first international conference on Machine learning, 2004, p. 105. https://doi.org/10.1145/1015330.1015427
J. D. Haynes and G. Rees, “Neuroimaging: decoding mental states from brain activity in humans,” Nature Reviews Neuroscience, vol. 7, p. 523, 2006. DOI: https://doi.org/10.1038/nrn1931
S. Afzal, M. Javed, M. Maqsood, F. Aadil, S. Rho, and I. Mehmood, “A Segmentation-Less Efficient Alzheimer Detection Approach Using Hybrid Image Features,” in Handbook of Multimedia Information Security: Techniques and Applications, ed: Springer, 2019, pp. 421-429. DOI: https://doi.org/10.1007/978-3-030-15887-3_20
S. Bauer, L.-P. Nolte, and M. Reyes, “Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011, pp. 354-361. https://doi.org/10.1007/978-3-642-23626-6_44
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
G. Orru, W. Pettersson-Yeo, A. F. Marquand, G. Sartori, and A. Mechelli, “Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review,” Neuroscience & Biobehavioral Reviews, vol. 36, pp. 1140-1152, 2012. https://doi.org/10.1016/j.neubiorev.2012.01.004
T. Smith-Vikos and F. J. Slack, “MicroRNAs circulate around Alzheimer’s disease,” Genome biology, vol. 14, p. 125, 2013. https://doi.org/10.1186/gb-2013-14-7-125
C. Laske, T. Leyhe, E. Stransky, N. Hoffmann, A. J. Fallgatter, and J. Dietzsch, “Identification of a blood-based biomarker panel for classification of Alzheimer’s disease,” International Journal of Neuropsychopharmacology, vol. 14, pp. 1147-1155, 2011. https://doi.org/10.1017/S1461145711000459
M. Lopez, J. Ramirez, J. Gorriz, D. Salas-Gonzalez, I. Á. Lvarez, F. Segovia, et al., “Neurological image classification for the Alzheimer’s Disease diagnosis using Kernel PCA and Support Vector Machines,” in Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE, 2009, pp. 2486-2489. 10.1109/NSSMIC.2009.5402069.
J. Dukart, K. Mueller, H. Barthel, A. Villringer, O. Sabri, M. L. Schroeter, et al., “Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI,” Psychiatry Research: Neuroimaging, vol. 212, pp. 230-236, 2013. https://doi.org/10.1016/j.pscychresns.2012.04.007
Y. Zhang, S. Wang, and Z. Dong, “Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree,” Progress In Electromagnetics Research, vol. 144, pp. 171-184, 2014.
P. Padilla, M. López, J. M. Górriz, J. Ramirez, D. Salas-Gonzalez, and I. Álvarez, “NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease,” IEEE Transactions on medical imaging, vol. 31, pp. 207-216, 2012. DOI: 10.1109/TMI.2011.2167628.
L. K. Ferreira, J. M. Rondina, R. Kubo, C. R. Ono, C. C. Leite, J. Smid, et al., “Support vector machine-based classification of neuroimages in Alzheimer’s disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals,” Revista Brasileira de Psiquiatria, pp. 0-0, 2017. https://doi.org/10.1590/1516-4446-2016-2083
P. Vemuri, J. L. Gunter, M. L. Senjem, J. L. Whitwell, K. Kantarci, D. S. Knopman, et al., “Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies,” Neuroimage, vol. 39, pp. 1186-1197, 2008. https://doi.org/10.1016/j.neuroimage.2007.09.073
Y. Fan, D. Shen, R. C. Gur, R. E. Gur, and C. Davatzikos, “COMPARE: classification of morphological patterns using adaptive regional elements,” IEEE transactions on medical imaging, vol. 26, pp. 93-105, 2007. DOI: 10.1109/TMI.2006.886812
E. Gerardin, G. Chételat, M. Chupin, R. Cuingnet, B. Desgranges, H.- S. Kim, et al., “Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging,” Neuroimage, vol. 47, pp. 1476-1486, 2009. https://doi.org/10.1016/j.neuroimage.2009.05.036
K. Hackmack, F. Paul, M. Weygandt, C. Allefeld, J.-D. Haynes, and A. s. D. N. Initiative, “Multi-scale classification of disease using structural MRI and wavelet transform,” Neuroimage, vol. 62, pp. 48-58, 2012. https://doi.org/10.1016/j.neuroimage.2012.05.022
A. Ortiz, J. M. Górriz, J. Ramírez, F. J. Martínez-Murcia, and A. s. D. N. Initiative, “LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease,” Pattern Recognition Letters, vol. 34, pp. 1725-1733, 2013. https://doi.org/10.1016/j.patrec.2013.04.014
D. Schmitter, A. Roche, B. Maréchal, D. Ribes, A. Abdulkadir, M. BachCuadra, 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. https://doi.org/10.1016/j.nicl.2014.11.001
J.-F. Horn, M.-O. Habert, A. Kas, Z. Malek, P. Maksud, L. Lacomblez, et al., “Differential automatic diagnosis between Alzheimer’s disease and frontotemporal dementia based on perfusion SPECT images,” Artificial intelligence in medicine, vol. 47, pp. 147-158, 2009. https://doi.org/10.1016/j.artmed.2009.05.001
A. Rao, Y. Lee, A. Gass, and A. Monsch, “Classification of Alzheimer’s Disease from structural MRI using sparse logistic regression with optional spatial regularization,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011, pp. 4499-4502. DOI: 10.1109/IEMBS.2011.6091115.
S. Kato, A. Homma, T. Sakuma, and M. Nakamura, “Detection of mild Alzheimer’s disease and mild cognitive impairment from elderly speech: Binary discrimination using logistic regression,” in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015, pp. 5569-5572. DOI: 10.1109/EMBC.2015.7319654.
X. Zhang, B. Hu, X. Ma, and L. Xu, “Resting-state whole-brain functional connectivity networks for mci classification using l2-regularized logistic regression,” IEEE transactions on nanobioscience, vol. 14, pp. 237-247, 2015. DOI: 10.1109/TNB.2015.2403274.
R. Casanova, F.-C. Hsu, M. A. Espeland, and A. s. D. N. Initiative, “Classification of structural MRI images in Alzheimer’s disease from the perspective of ill-posed problems,” PloS one, vol. 7, p. e44877, 2012. https://doi.org/10.1371/journal.pone.0044877
T. M. Nir, J. E. Villalon-Reina, G. Prasad, N. Jahanshad, S. H. Joshi, A. W. Toga, et al., “Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer’s disease,” Neurobiology of aging, vol. 36, pp. S132-S140, 2015. https://doi.org/10.1016/j.neurobiolaging.2014.05.037
P. Johnson, L. Vandewater, W. Wilson, P. Maruff, G. Savage, P. Graham, et al., “Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease,” BMC bioinformatics, vol. 15, p. S11, 2014. https://doi.org/10.1186/1471-2105-15-S16-S11
H. G. Lee, C. Y. Yi, D. E. Lee, and D. Arditi, “An advanced stochastic time‐cost tradeoff analysis based on a CPM‐guided genetic algorithm,” Computer‐Aided Civil and Infrastructure Engineering, vol. 30, pp. 824-842, 2015. https://doi.org/10.1111/mice.12148
M. Martínez-Ballesteros, J. Bacardit, A. Troncoso, and J. C. Riquelme, “Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets,” Integrated Computer-Aided Engineering, vol. 22, pp. 21-39, 2015. DOI: 10.3233/ICA-180580.
T. Mazzocco and A. Hussain, “Novel logistic regression models to aid the diagnosis of dementia,” Expert Systems with Applications, vol. 39, pp. 3356-3361, 2012. https://doi.org/10.1016/j.eswa.2011.09.023
R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, pp. 179-188, 1936.
K. Fukunaga, “Introduction to statistical pattern classification,” ed: Academic Press USA: 1990. https://doi.org/10.1117/12.737157
F. Nie, S. Xiang, and C. Zhang, “Neighborhood MinMax Projections,” in IJCAI, 2007, pp. 993-998.
S. Xiang, F. Nie, and C. Zhang, “Learning a Mahalanobis distance metric for data clustering and classification,” Pattern Recognition, vol. 41, pp. 3600-3612, 2008. https://doi.org/10.1016/j.patcog.2008.05.018
M. Zhao, R. H. Chan, P. Tang, T. W. Chow, and S. W. Wong, “Trace ratio linear discriminant analysis for medical diagnosis: a case study of dementia,” IEEE signal processing letters, vol. 20, p. 431, 2013. DOI: 10.1109/LSP.2013.2250281.
J. Akhila, C. Markose, and R. Aneesh, “Feature extraction and classification of Dementia with neural network,” in 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017, pp. 1446-1450. DOI: 10.1109/RBME.2018.2886237.
C. V. Dolph, M. Alam, Z. Shboul, M. D. Samad, and K. M. Iftekharuddin, “Deep learning of texture and structural features for multiclass Alzheimer’s disease classification,” in 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 2259-2266. DOI: 10.1109/IJCNN.2017.7966129.
M. Faturrahman, I. Wasito, N. Hanifah, and R. Mufidah, “Structural MRI classification for Alzheimer’s disease detection using deep belief network,” in 2017 11th International Conference on Information & Communication Technology and System (ICTS), 2017, pp. 37-42. DOI: 10.1109/ICTS.2017.8265643.
H.-I. Suk, S.-W. Lee, D. Shen, and A. s. D. N. Initiative, “Deep ensemble learning of sparse regression models for brain disease diagnosis,” Medical image analysis, vol. 37, pp. 101-113, 2017. https://doi.org/10.1016/j.media.2017.01.008
E. M. Alkabawi, A. R. Hilal, and O. A. Basir, “Feature abstraction for early detection of multi-type of dementia with sparse auto-encoder,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 3471-3476. DOI: 10.1109/SMC.2017.8123168.
E. M. Alkabawi, A. R. Hilal, and O. A. Basir, “Computer-aided classification of multi-types of dementia via convolutional neural networks,” in 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2017, pp. 45-50. DOI: 10.1109/MeMeA.2017.7985847.
R. Cui, M. Liu, and G. Li, “Longitudinal analysis for Alzheimer’s disease diagnosis using RNN,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, 2018, pp. 1398-1401. https://doi.org/10.1016/j.media.2020.101694
Y. Wang, Y. Yang, X. Guo, C. Ye, N. Gao, Y. Fang, et al., “A Novel Multimodal MRI Analysis for Alzheimer’s Disease Based on Convolutional Neural Network,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 754-757. DOI: 10.1109/EMBC.2018.8512372.
K. Gunawardena, R. Rajapakse, and N. Kodikara, “Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural MRI data,” in Mechatronics and Machine Vision in Practice (M2VIP), 2017 24th International Conference on, 2017, pp. 1-7. DOI: 10.1109/M2VIP.2017.8211486.
G. A. Papakostas, A. Savio, M. Graña, and V. G. Kaburlasos, “A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data,” Neurocomputing, vol. 150, pp. 37-42, 2015. https://doi.org/10.1016/j.neucom.2014.02.076
F. Peng and Y. Ouyang, “Optimal clustering of railroad track maintenance jobs,” Computer‐Aided Civil and Infrastructure Engineering, vol. 29, pp. 235-247, 2014. https://doi.org/10.1111/mice.12036
H. Wang, A. Yajima, R. Y. Liang*, and H. Castaneda, “Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data,” Computer‐Aided Civil and Infrastructure Engineering, vol. 30, pp. 300- 316, 2015. https://doi.org/10.1111/mice.12096
J. Huo, Y. Gao, W. Yang, and H. Yin, “Multi-instance dictionary learning for detecting abnormal events in surveillance videos,” International journal of neural systems, vol. 24, p. 1430010, 2014. https://doi.org/10.1142/S0129065714300101
T. Varghese, K. R. Sheela, P. Mathuranath, and A. Singh, “Evaluation of different stages of Alzheimer’s disease using unsupervised clustering techniques and voxel based morphometry,” in Information and Communication Technologies (WICT), 2012 World Congress on, 2012, pp. 953-958. DOI: 10.1109/WICT.2012.6409212.
K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-Pineau, K. Afdel, and G. Catheline, “Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning,” in 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 2018, pp. 345-350. DOI: 10.1109/CBMS.2018.00067.
S. Afzal, M. Maqsood, F. Nazir, U. Khan, F. Aadil, K. M. Awan, et al., “A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection,” IEEE Access, vol. 7, pp. 115528-115539, 2019. DOI: 10.1109/ACCESS.2019.2932786.
N. M. Khan, N. Abraham, and M. Hon, “Transfer Learning With Intelligent Training Data Selection for Prediction of Alzheimer’s Disease,” IEEE Access, vol. 7, 2019. DOI: 10.1109/ACCESS.2019.2920448.
M. Maqsood, F. Nazir, U. Khan, F. Aadil, H. Jamal, I. Mehmood, et al., “Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans,” Sensors, vol. 19, 2019. https://doi.org/10.3390/s19112645
T. D. Phong, H. N. Duong, H. T. Nguyen, N. T. Trong, V. H. Nguyen, T. Van Hoa, et al., “Brain hemorrhage diagnosis by using deep learning,” in Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, 2017, pp. 34-39. https://doi.org/10.1145/3036290.3036326
S. Wang, Y. Shen, W. Chen, T. Xiao, and J. Hu, “Automatic recognition of mild cognitive impairment from mri images using expedited convolutional neural networks,” in International Conference on Artificial Neural Networks, 2017, pp. 373-380. https://doi.org/10.1007/978-3-319-68600-4_43
M. A. Nowrangi, C. G. Lyketsos, J.-M. S. Leoutsakos, K. Oishi, M. Albert, S. Mori, et al., “Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer’s disease,” Alzheimer’s & Dementia, vol. 9, pp. 519-528, 2013. https://doi.org/10.1093/cercor/bhy031
B. Cheng, M. Liu, D. Shen, Z. Li, D. Zhang, and A. s. D. N. Initiative, “Multi-domain transfer learning for early diagnosis of Alzheimer’s disease,” Neuroinformatics, vol. 15, pp. 115-132, 2017. https://doi.org/10.1007/s12021-016-9318-5
T. Glozman, J. Solomon, F. Pestilli, and L. Guibas, “Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease,” Journal of Alzheimer’s Disease, vol. 56, pp. 287-295, 2017. DOI: 10.3233/JAD-160900.
M. Dyrba, M. Ewers, M. Wegrzyn, I. Kilimann, C. Plant, A. Oswald, et al., “Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data,” PloS one, vol. 8, p. e64925, 2013. https://doi.org/10.1371/journal.pone.0064925
W. Li, Y. Zhao, X. Chen, Y. Xiao, and Y. Qin, “Detecting Alzheimer’s Disease on Small Dataset: A Knowledge Transfer Perspective,” IEEE journal of biomedical and health informatics, 2018. DOI: 10.1109/JBHI.2018.2839771.
T. Altaf, S. M. Anwar, N. Gul, M. N. Majeed, and M. Majid, “Multi-class Alzheimer’s disease classification using image and clinical features,” Biomedical Signal Processing and Control, vol. 43, pp. 64-74, 2018. https://doi.org/10.1016/j.bspc.2018.02.019
O. B. Ahmed, M. Mizotin, J. Benois-Pineau, M. Allard, G. Catheline, C. B. Amar, et al., “Alzheimer’s disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex,” Computerized Medical Imaging and Graphics, vol. 44, pp. 13-25, 2015. https://doi.org/10.1016/j.eswa.2016.04.029
D. Chitradevi and S. Prabha, “Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease,” Applied Soft Computing, vol. 86, p. 105857, 2020. https://doi.org/10.1016/j.asoc.2019.105857
X. Hao, Y. Bao, Y. Guo, M. Yu, D. Zhang, S. L. Risacher, et al., “Multimodal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer’s disease,” Medical Image Analysis, vol. 60, p. 101625, 2020. https://doi.org/10.1016/j.media.2019.101625
C. Park, J. Ha, and S. Park, “Prediction of Alzheimer’s disease based on deep neural network by integrating gene expression and DNA methylation dataset,” Expert Systems with Applications, vol. 140, p. 112873, 2020. https://doi.org/10.1016/j.eswa.2019.112873
A. Shikalgar and S. Sonavane, “Hybrid Deep Learning Approach for Classifying Alzheimer Disease Based on Multimodal Data,” in Computing in Engineering and Technology, ed: Springer, 2020, pp. 511-520. https://doi.org/10.1007/978-981-32-9515-5_49
A. Giersch and J. T. Coull, “TRF1: It Was the Best of Time (s)…,” Timing & Time Perception, vol. 6, pp. 231-414, 2018. https://doi.org/10.1163/22134468-00603001
S. Leandrou, S. Petroudi, P. A. Kyriacou, C. C. Reyes-Aldasoro, and C. S. Pattichis, “Quantitative MRI brain studies in mild cognitive impairment and Alzheimer’s disease: a methodological review,” IEEE reviews in biomedical engineering, vol. 11, pp. 97-111, 2018. DOI: 10.1109/RBME.2018.2796598.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, et al., “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60-88, 2017. https://doi.org/10.1016/j.media.2017.07.005
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, et al., “Deep learning for health informatics,” IEEE journal of biomedical and health informatics, vol. 21, pp. 4-21, 2016. DOI: 10.1109/ICICI.2017.8365301.
P. V. Rouast, M. Adam, and R. Chiong, “Deep learning for human affect recognition: insights and new developments,” IEEE Transactions on Affective Computing, 2019. DOI: 10.1109/TAFFC.2018.2890471.
C. R. Jack Jr, M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, et al., “The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 27, pp. 685-691, 2008. https://doi.org/10.1002/jmri.21049
K. A. Ellis, A. I. Bush, D. Darby, D. De Fazio, J. Foster, P. Hudson, et al., “The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease,” International psychogeriatrics, vol. 21, pp. 672-687, 2009. https://doi.org/10.1017/S1041610209009405
D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of cognitive neuroscience, vol. 19, pp. 1498-1507, 2007. https://doi.org/10.1162/jocn.2007.19.9.1498
A. Sedik, A. M. Iliyasu, A. El-Rahiem, M. E. Abdel Samea, A. AbdelRaheem, M. Hammad, et al., “Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections,” Viruses, vol. 12, p. 769, 2020, https://doi.org/10.3390/v12070769
A. Alghamdi, M. Hammad, H. Ugail, A. Abdel-Raheem, K. Muhammad, H. S. Khalifa, et al., “Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities,” Multimedia Tools and Applications, pp. 1-22, 2020, https://doi.org/10.1007/s11042-020-08769-x
S. Toraman, T. B. Alakus, and I. Turkoglu, “Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks,” Chaos, Solitons & Fractals, vol. 140, p. 110122, 2020, https://doi.org/10.1016/j.chaos.2020.110122
Downloads
Published
-
Abstract739
-
PDF322
Submit Article
Information
