GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks
By: Tianqi Ding , Dawei Xiang , Keith E Schubert and more
Potential Business Impact:
Finds Alzheimer's earlier by studying brain connections.
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for early AD diagnosis.
Similar Papers
Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
Machine Learning (CS)
Finds brain parts that *cause* Alzheimer's.
An Interpretable Multi-Plane Fusion Framework With Kolmogorov-Arnold Network Guided Attention Enhancement for Alzheimer's Disease Diagnosis
CV and Pattern Recognition
Finds Alzheimer's early using brain scans.
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Machine Learning (CS)
Finds Alzheimer's early using brain wave patterns.