Artificial Intelligence Approaches for Early Detection and Diagnosis of Alzheimer's Disease: A Review


  • Mingyang Wei
  • Yabei Li
  • Minjun Liang
  • Mengbo Xi
  • He Tian



Deep Learning, Machine Learning, Alzheimer's Disease.


Alzheimer's Disease (AD) is an irreversible neurodegenerative disease common in the elderly. The application of artificial intelligence technology to the early diagnosis of AD can not only improve the accuracy of prediction compared with traditional methods, but also save the complicated manual feature extraction of traditional methods and speed up the diagnosis. This paper reviews various applications of artificial intelligence algorithms in AD diagnosis, including machine learning, convolutional neural network, graph convolutional neural network, cyclic neural network and other mainstream deep learning technologies. The advantages and disadvantages of each approach are discussed, and finally, we discuss limitations and future prospects.


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Y.-G. Chen, “Research progress in the pathogenesis of Alzheimer’s disease,” Chinese medical journal, vol. 131, no. 13, p. 1618, 2018.

C. Patterson, “The state of the art of dementia research: New frontiers,” World Alzheimer Report, vol. 2018, 2018.

X. Wang, J. Qi, Y. Yang, and P. Yang, “A survey of disease progression modeling techniques for alzheimer’s diseases,” in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE, 2019, pp. 1237–1242.

J. C. Morris et al., “Mild cognitive impairment represents early-stage Alzheimer disease,” Archives of neurology, vol. 58, no. 3, pp. 397–405, 2001.

A. Ward, S. Tardiff, C. Dye, and H. M. Arrighi, “Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic review of the literature,” Dement Geriatr Cogn Dis Extra, vol. 3, no. 1, pp. 320–332, 2013, doi: 10.1159/000354370.

J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” Neuroimage, vol. 11, no. 6, pp. 805–821, 2000.

H.-I. Suk, S.-W. Lee, D. Shen, and A. D. N. Initiative, “Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis,” NeuroImage, vol. 101, pp. 569–582, 2014.

J. Zhang, B. Yan, X. Huang, P. Yang, and C. Huang, “The diagnosis of Alzheimer’s disease based on voxel-based morphometry and support vector machine,” in 2008 Fourth International Conference on Natural Computation, IEEE, 2008, pp. 197–201.

C. Good, I. Johnsrude, J. Ashburner, K. Friston, and R. Frackowiak, “Voxel based morphometry of 465 normal adult human brains,” Neuroimage, vol. 11, no. 5, p. S607, 2000.

A. Ortiz, J. Munilla, J. M. Gorriz, and J. Ramirez, “Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease,” International journal of neural systems, vol. 26, no. 07, p. 1650025, 2016.

C.-Y. Wee, P.-T. Yap, D. Shen, and A. D. N. Initiative, “Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns,” Human brain mapping, vol. 34, no. 12, pp. 3411–3425, 2013.

R. Cui and M. Liu, “Hippocampus analysis by combination of 3-D DenseNet and shapes for Alzheimer’s disease diagnosis,” IEEE journal of biomedical and health informatics, vol. 23, no. 5, pp. 2099–2107, 2018.

F. Li, M. Liu, and A. D. N. Initiative, “A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease,” Journal of neuroscience methods, vol. 323, pp. 108–118, 2019.

M. Liu, D. Zhang, D. Shen, and A. D. N. Initiative, “Ensemble sparse classification of Alzheimer’s disease,” NeuroImage, vol. 60, no. 2, pp. 1106–1116, 2012.

M. Liu, D. Zhang, D. Shen, and A. D. N. Initiative, “Hierarchical fusion of features and classifier decisions for Alzheimer’s disease diagnosis,” Human brain mapping, vol. 35, no. 4, pp. 1305–1319, 2014.

F. Li, M. Liu, and A. D. N. Initiative, “Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks,” Computerized Medical Imaging and Graphics, vol. 70, pp. 101–110, 2018.

M. Liu, J. Zhang, E. Adeli, and D. Shen, “Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp. 1195–1206, 2018.

S. Ahmed et al., “Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases,” IEEE Access, vol. 7, pp. 73373–73383, 2019.

J. Islam and Y. Zhang, “A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data,” in International conference on brain informatics, Springer, 2017, pp. 213–222.

S. Luo, X. Li, and J. Li, “Automatic Alzheimer’s disease recognition from MRI data using deep learning method,” Journal of Applied Mathematics and Physics, vol. 5, no. 9, pp. 1892–1898, 2017.

The Alzheimer’s Disease Neuroimaging Initiative et al., “Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks,” Quant. Imaging Med. Surg, vol. 8, no. 10, pp. 992–1003, Nov. 2018, doi: 10.21037/qims.2018.10.17.

L. Gao et al., “Brain disease diagnosis using deep learning features from longitudinal MR images,” in Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, Springer, 2018, pp. 327–339.

R. Jain, N. Jain, A. Aggarwal, and D. J. Hemanth, “Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images,” Cognitive Systems Research, vol. 57, pp. 147–159, 2019.

S. Korolev, A. Safiullin, M. Belyaev, and Y. Dodonova, “Residual and plain convolutional neural networks for 3D brain MRI classification,” in 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), IEEE, 2017, pp. 835–838.

K. Bäckström, M. Nazari, I. Y.-H. Gu, and A. S. Jakola, “An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, pp. 149–153.

S. Basaia et al., “Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks,” NeuroImage: Clinical, vol. 21, p. 101645, 2019.

H. Wang et al., “Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease,” Neurocomputing, vol. 333, pp. 145–156, 2019.

M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.

L. Khedher, J. Ramírez, J. M. Górriz, A. Brahim, F. Segovia, and A. s D. N. Initiative, “Early diagnosis of Alzheimer׳ s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images,” Neurocomputing, vol. 151, pp. 139–150, 2015.

L. Mesrob et al., “Identification of atrophy patterns in Alzheimer’s disease based on SVM feature selection and anatomical parcellation,” in International Workshop on Medical Imaging and Virtual Reality, Springer, 2008, pp. 124–132.

S. I. Dimitriadis, D. Liparas, M. N. Tsolaki, and A. D. N. Initiative, “Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer’s disease patients: From the alzheimer’s disease neuroimaging initiative (ADNI) database,” Journal of neuroscience methods, vol. 302, pp. 14–23, 2018.

P. Telagarapu, B. Mohanty, and K. R. Anandh, “Analysis of Alzheimer condition in T1-weighted MR images using texture features and K-NN classifier,” in 2018 international CET conference on control, communication, and computing (IC4), IEEE, 2018, pp. 331–334.

S.-H. Wang, P. Phillips, Y. Sui, B. Liu, M. Yang, and H. Cheng, “Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling,” Journal of medical systems, vol. 42, no. 5, pp. 1–11, 2018.

J. Liu et al., “Applications of deep learning to MRI images: A survey,” Big Data Mining and Analytics, vol. 1, no. 1, pp. 1–18, 2018.

S. Sarraf and G. Tofighi, “Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data,” in 2016 future technologies conference (FTC), IEEE, 2016, pp. 816–820.

Y. Dai, D. Qiu, Y. Wang, S. Dong, and H.-L. Wang, “Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 33, no. 05, p. 1957001, 2019.

A. Shakarami, H. Tarrah, and A. Mahdavi-Hormat, “A CAD system for diagnosing Alzheimer’s disease using 2D slices and an improved AlexNet-SVM method,” Optik, vol. 212, p. 164237, 2020.

Y. Kazemi and S. Houghten, “A deep learning pipeline to classify different stages of Alzheimer’s disease from fMRI data,” in 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE, 2018, pp. 1–8.

M. Hon and N. M. Khan, “Towards Alzheimer’s disease classification through transfer learning,” in 2017 IEEE International conference on bioinformatics and biomedicine (BIBM), IEEE, 2017, pp. 1166–1169.

Y. Ding et al., “A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain,” Radiology, vol. 290, no. 2, pp. 456–464, 2019.

E. Yee, K. Popuri, M. F. Beg, and A. D. N. Initiative, “Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer’s dementia score,” Human brain mapping, vol. 41, no. 1, pp. 5–16, 2020.

L. V. Fulton, D. Dolezel, J. Harrop, Y. Yan, and C. P. Fulton, “Classification of Alzheimer’s disease with and without imagery using gradient boosted machines and ResNet-50,” Brain sciences, vol. 9, no. 9, p. 212, 2019.

H. Wang et al., “Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease,” Neurocomputing, vol. 333, pp. 145–156, 2019.

F. Li, M. Liu, and A. D. N. Initiative, “A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease,” Journal of neuroscience methods, vol. 323, pp. 108–118, 2019.

M. Liu, D. Cheng, W. Yan, and A. D. N. Initiative, “Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images,” Frontiers in neuroinformatics, vol. 12, p. 35, 2018.

R. Li et al., “Deep learning based imaging data completion for improved brain disease diagnosis,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2014, pp. 305–312.

E. Hosseini-Asl, R. Keynton, and A. El-Baz, “Alzheimer’s disease diagnostics by adaptation of 3D convolutional network,” in 2016 IEEE international conference on image processing (ICIP), IEEE, 2016, pp. 126–130.

Y. Huang, J. Xu, Y. Zhou, T. Tong, X. Zhuang, and A. D. N. Initiative (ADNI, “Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network,” Frontiers in Neuroscience, vol. 13, p. 509, 2019.

S. Liu, C. Yadav, C. Fernandez-Granda, and N. Razavian, “On the design of convolutional neural networks for automatic detection of Alzheimer’s disease,” in Machine Learning for Health Workshop, PMLR, 2020, pp. 184–201.

X. Zhao, F. Zhou, L. Ou-Yang, T. Wang, and B. Lei, “Graph convolutional network analysis for mild cognitive impairment prediction,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019, pp. 1598–1601.

J. Guo, W. Qiu, X. Li, X. Zhao, N. Guo, and Q. Li, “Predicting Alzheimer’s disease by hierarchical graph convolution from positron emission tomography imaging,” in 2019 IEEE international conference on big data (big data), IEEE, 2019, pp. 5359–5363.

J. Liu, G. Tan, W. Lan, and J. Wang, “Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks,” BMC Bioinformatics, vol. 21, no. S6, p. 123, Nov. 2020, doi: 10.1186/s12859-020-3437-6.

H. Li and Y. Fan, “Early prediction of Alzheimer’s disease dementia based on baseline hippocampal MRI and 1-year follow-up cognitive measures using deep recurrent neural networks,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019, pp. 368–371.

N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms,” in International conference on machine learning, PMLR, 2015, pp. 843–852.

G. Lee, K. Nho, B. Kang, K.-A. Sohn, and D. Kim, “Predicting Alzheimer’s disease progression using multi-modal deep learning approach,” Scientific reports, vol. 9, no. 1, pp. 1–12, 2019.




How to Cite

Wei, M., Li, Y., Liang, M., Xi, M., & Tian, H. (2023). Artificial Intelligence Approaches for Early Detection and Diagnosis of Alzheimer’s Disease: A Review. Academic Journal of Science and Technology, 5(3), 215–221.