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 May 18;12(5):815.
doi: 10.3390/jpm12050815.

Deep Learning-Based Diagnosis of Alzheimer's Disease

Tausifa Jan Saleem  1 , Syed Rameem Zahra  1 , Fan Wu  2 , Ahmed Alwakeel  3   4 , Mohammed Alwakeel  4 , Fathe Jeribi  5 , Mohammad Hijji  4
Affiliations
Review

Deep Learning-Based Diagnosis of Alzheimer's Disease

Tausifa Jan Saleem et al. J Pers Med. .

Abstract

Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

Keywords: Alzheimer’s disease; Magnetic Resonance Imaging; biomarkers; deep learning; mild cognitive impairment; positron emission tomography.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Presents the preliminaries required for DL-based diagnosis of AD. These preliminaries include biomarkers of AD, AD datasets and DL techniques.
Figure 2
Figure 2
Deep learning techniques.
Figure 3
Figure 3
DL-based AD classification framework.
Figure 4
Figure 4
Number of studies versus biomarker, datasets, DL technique and performance metric.
See this image and copyright information in PMC

References

    1. Rabeh A.B., Benzarti F., Amiri H. Diagnosis of alzheimer diseases in early step using SVM (Support Vector Machine); Proceedings of the 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV); Beni Mellal, Morocco. 29 March–1 April 2016; Piscataway, NJ, USA: IEEE; 2016. pp. 364–367.
    1. Alam S., Kwon G.R., Kim J.I., Park C.S. Twin SVM-based classification of Alzheimer’s disease using complex dual-tree wavelet principal coefficients and LDA. J. Healthc. Eng. 2017;2017:1–12. doi: 10.1155/2017/8750506. - DOI - PMC - PubMed
    1. Acharya U.R., Fernandes S.L., WeiKoh J.E., Ciaccio E.J., Fabell M.K.M., Tanik U.J., Rajinikanth V., Yeong C.H. Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques. J. Med. Syst. 2019;43:302. - PubMed
    1. Tufail A.B., Abidi A., Siddiqui A.M., Younis M.S. Automatic classification of initial categories of Alzheimer’s disease from structural MRI phase images: A comparison of PSVM, KNN and ANN methods. Int. J. Biomed. Biol. Eng. 2012;6:713–717.
    1. Ramírez J., Górriz J.M., Segovia F., Chaves R., Salas-Gonzalez D., López M., Álvarez I., Padilla P. Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neurosci. Lett. 2010;472:99–103. doi: 10.1016/j.neulet.2010.01.056. - DOI - PubMed

LinkOut - more resources