Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Sep 23;10(10):1842.
doi: 10.3390/healthcare10101842.

Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review

K Aditya Shastry  1 , V Vijayakumar  2   3   4 , Manoj Kumar M V  1 , Manjunatha B A  1 , Chandrashekhar B N  1
Affiliations
Review

Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review

K Aditya Shastry et al. Healthcare (Basel). .

Abstract

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

Keywords: Alzheimer’s disease; deep learning; health informatics.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Types of DL architectures.
Figure 2
Figure 2
The architecture of a generalized CNN.
Figure 3
Figure 3
MLP procedure (where Act signifies activation function, w represents weights, and Net denotes network).
Figure 4
Figure 4
Architecture of RBM.
Figure 5
Figure 5
CNN architecture.
Figure 6
Figure 6
RBM architecture.
Figure 7
Figure 7
DBN architecture.
Figure 8
Figure 8
AE architecture.
Figure 9
Figure 9
Sparse AE architecture.
Figure 10
Figure 10
Stacked AE architecture.
See this image and copyright information in PMC

References

    1. Mathew A., Amudha P., Sivakumari S. Deep Learning Techniques: An Overview. In: Hassanien A., Bhatnagar R., Darwish A., editors. Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing. Volume 1141. Springer; Singapore: 2020. - DOI
    1. Jiang T. Deep Learning Application in Alzheimer Disease Diagnoses and Prediction; Proceedings of the 2020 4th International Conference on Artificial Intelligence and Virtual Reality; Kumamoto, Japan. 23–25 October 2020; - DOI
    1. Kraemer H.C., Taylor J.L., Tinklenberg J.R., Yesavage J.A. The Stages of Alzheimer’s Disease: A Reappraisal. Dement. Geriatr. Cogn. Disord. 1998;9:299–308. doi: 10.1159/000017081. - DOI - PubMed
    1. Pais M., Martinez L., Ribeiro O., Loureiro J., Fernandez R., Valiengo L., Canineu P., Stella F., Talib L., Radanovic M., et al. Early diagnosis and treatment of Alzheimer’s disease: New definitions and challenges. Braz. J. Psychiatry. 2020;42:431–441. doi: 10.1590/1516-4446-2019-0735. - DOI - PMC - PubMed
    1. Al-Shoukry S., Rassem T.H., Makbol N.M. Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review. IEEE Access. 2020;8:77131–77141. doi: 10.1109/ACCESS.2020.2989396. - DOI

LinkOut - more resources