Shahrood University of Technology Journal of AI and Data Mining 2322-5211 13 4 2025 10 01 GNN-xLSTM: Integrating Graph Convolutional Network and Extended Long Short-Term Memory for Alzheimer’s Disease Classification 501 513 3588 10.22044/jadm.2025.15978.2714 EN Fatemeh Iranmanesh Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran Najme Mansouri Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran. Behnam Mohammad Hasani Zade Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran. Journal Article 2025 03 24 The diagnosis of Alzheimer's Disease (AD) remains a significant challenge in medical research. To address the limitations of static models in capturing dynamic brain changes, this paper proposes a novel GNN-xLSTM model that integrates Graph Neural Networks (GNN) with an extended Long Short-Term Memory (xLSTM) architecture. The key innovation lies in combining GNN’s ability to model spatial relationships in brain imaging data with xLSTM’s enhanced sequential learning via matrix-based memory representation and exponential gate stabilization. In the proposed approach, brain images are divided into regions, with each region represented as a graph node connected in a grid structure, and feature vectors are extracted for each node. The proposed architecture incorporates Graph Convolutional Network (GCN) layers, xLSTM cells, residual connections, batch normalization, and dropout to jointly capture global, local, and temporal dependencies. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the GNN-xLSTM model outperforms baseline models in terms of accuracy, precision, recall, and F1-score. These results demonstrate the model’s effectiveness in identifying critical brain regions and improving AD classification performance. https://jad.shahroodut.ac.ir/article_3588_932bf25f3caf080b501d32fa7622a844.pdf