A Survey of Deep Learning for Alzheimer’s Disease

Qinghua Zhou, Jiaji Wang, Xiang Yu, Shuihua Wang, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)611-668
Number of pages58
JournalMachine Learning and Knowledge Extraction
Volume5
Issue number2
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Keywords

  • Alzheimer’s disease
  • deep learning
  • mild cognitive impairment
  • neural networks
  • recent advances

Fingerprint

Dive into the research topics of 'A Survey of Deep Learning for Alzheimer’s Disease'. Together they form a unique fingerprint.

Cite this