TY - JOUR
T1 - Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging
T2 - A Systematic Review
AU - Alsubaie, Mohammed G.
AU - Luo, Suhuai
AU - Shaukat, Kamran
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - AD detection
KW - Alzheimer’s disease
KW - autoencoders
KW - convolutional neural network
KW - graph neural network
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85188903663&partnerID=8YFLogxK
U2 - 10.3390/make6010024
DO - 10.3390/make6010024
M3 - Review article
AN - SCOPUS:85188903663
SN - 2504-4990
VL - 6
SP - 464
EP - 505
JO - Machine Learning and Knowledge Extraction
JF - Machine Learning and Knowledge Extraction
IS - 1
ER -