Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review

Mohammed G. Alsubaie, Suhuai Luo, Kamran Shaukat

Research output: Contribution to journalReview articlepeer-review

19 Citations (Scopus)

Abstract

Alzheimer’s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning’s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.

Original languageEnglish
Pages (from-to)464-505
Number of pages42
JournalMachine Learning and Knowledge Extraction
Volume6
Issue number1
DOIs
Publication statusPublished - Mar 2024

Keywords

  • AD detection
  • Alzheimer’s disease
  • autoencoders
  • convolutional neural network
  • graph neural network
  • recurrent neural network

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