A Survey of Deep Learning for Alzheimer’s Disease

Author:

Zhou Qinghua1,Wang Jiaji1ORCID,Yu Xiang1,Wang Shuihua1,Zhang Yudong12ORCID

Affiliation:

1. School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK

2. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Abstract

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

Funder

MRC, UK

Royal Society, UK

BHF, UK

Hope Foundation for Cancer Research, UK

GCRF, UK

Sino-UK Industrial Fund, UK

LIAS, UK

Data Science Enhancement Fund, UK

Fight for Sight, UK

Sino-UK Education Fund, UK

BBSRC, UK

Publisher

MDPI AG

Subject

Artificial Intelligence,Engineering (miscellaneous)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment;Technologies;2023-12-24

2. Classification of Alzheimer’s Disease from Cranial MRI Images Using Transfer Learning;2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT);2023-12-14

3. Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram;Journal of King Saud University - Computer and Information Sciences;2023-12

4. A review of deep learning in dentistry;Neurocomputing;2023-10

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