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
. 2025;25(4):335-349.
doi: 10.2174/0115680266310776240524061252.

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis

Affiliations
Review

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis

Anjali Mahavar et al. Curr Top Med Chem. 2025.

Abstract

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD. This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, etc. Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.

Keywords: Alzheimer's disease; Convolutional neural networks; Deep belief networks (DBN); Deep boltzmann machines; Deep learning; Recurrent neural networks.

PubMed Disclaimer

Similar articles

Cited by

References

    1. (a) Gerber A.; Becker, A.; Jung, J.; Strathmann, F.; Lauber, C.; The histological examination of exceptionally well-preserved brain tissues from F. Johan, marking the second reported case of Alzheimer's disease. J Neurobiol (b) Keuck, L. History as a biomedical matter: Recent reassessments of the first cases of Alzheimer’s disease. Hist. Philos. Life Sci., 2018, 40(1), 10. 1999,52(123),130 - DOI - PubMed
    1. Tuan T.A.; Pham T.B.; Kim J.Y.; Tavares J.M.R.S.; Alzheimer’s diagnosis using deep learning in segmenting and classifying 3D brain MR images. Int J Neurosci 2022,132(7),689-698 - DOI - PubMed
    1. Dyrba M.; Ewers M.; Wegrzyn M.; Kilimann I.; Plant C.; Oswald A.; Meindl T.; Pievani M.; Bokde A.L.W.; Fellgiebel A.; Filippi M.; Hampel H.; Klöppel S.; Hauenstein K.; Kirste T.; Teipel S.J.; Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data. PLoS One 2013,8(5),e64925 - DOI - PubMed
    1. Fan Z.; Xu F.; Qi X.; Li C.; Yao L.; Classification of Alzheimer’s disease based on brain MRI and machine learning. Neural Comput Appl 2020,32(7),1927-1936 - DOI
    1. Koga S.; Ikeda A.; Dickson D.W.; Deep learning-based model for diagnosing Alzheimer’s disease and tauopathies. Neuropathol Appl Neurobiol 2022,48(1),e12759 - DOI - PubMed

LinkOut - more resources