Graph Neural Network based Alzheimer’s Disease Classification using Structural Brain Network

Subaramya, S.; Kokul, T.; Ratnarajah, Nagulan; Pinidiyaarachchi, U.A.J.

doi:10.1109/icter58063.2022.10024076

Cited by 1 publication

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [73] selected the features of DTI and reconstructed the topology of the structural MRI (sMRI), and combined it with the Pearson correlation coefficient of fMRI to construct brain connectivity. Subaramya et al [74] used fiber bundles and brain regions' volumes to construct a weighted graph, and then obtained a binarized graph through the sign test.…”

Section: Subject Graphmentioning

confidence: 99%

See 1 more Smart Citation

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

Zhang,

Yang,

Zhang

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

show abstract

Section: Subject Graphmentioning

confidence: 99%

“…Kazi et al [25] utilized both concatenation and maximum pooling to merge the output of each graph convolution. Subaramya et al [74] sorted features and extracted significant features with maximum pooling. Mahmood et al [69] simultaneously used maximum pooling, average pooling, and attention-based pooling [121].…”

Section: Global Poolingmentioning

confidence: 99%