ORIGINAL RESEARCH article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1630838
X-FASNet: Cross-Scale Feature-Aware with Self-Attention Network for Cognitive Decline Assessment in Alzheimer's Disease
Provisionally accepted- 1Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fujian, China
- 2Macau University of Science and Technology, Taipa, Macao, SAR China
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Early diagnosis of Alzheimer's disease is critical for effective therapeutic intervention. The progressive nature of cognitive decline requires precise computational methods to detect subtle neuroanatomical changes in prodromal stages. Current multi-scale neural networks have limited cross-scale feature integration capabilities, which constrain their effectiveness in identifying early neurodegenerative markers. This paper presents an Efficient Cross-Scale Feature-Aware Self-Attention Network (X-FASNet) designed to address these limitations through systematic hierarchical representation learning. The proposed architecture implements a dual-pathway multi-scale feature extraction approach to identify discriminative neuroanatomical patterns across various spatial resolutions, while integrating a novel cross-scale feature-aware self-attention module that enhances inter-scale information exchange and captures long-range dependencies.Quantitative evaluations on the DPC-SF dataset demonstrate that X-FASNet achieves superior performance with 93.7% accuracy and 0.973 F1-score, outperforming CONVADD by 10.8 percentage points in accuracy and 0.118 in F1-score, while also surpassing EfficientB2 on key performance metrics. Comprehensive experimentation across multiple neuroimaging datasets confirms that X-FASNet provides an effective computational framework for neurodegeneration assessment, characterized by enhanced detection of subtle anatomical variations and improved pathological pattern recognition.
Keywords: Alzheimer's disease, Multi-scale model, Cross-scale feature-aware self-attention, Feature fusion, Cognitive decline assessment
Received: 19 May 2025; Accepted: 23 Jul 2025.
Copyright: © 2025 Chen, Xu, Peng, Zhang, Zhang, Zheng, Yan and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yiran Peng, Macau University of Science and Technology, Taipa, Macao, SAR China
Hong Zhang, Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fujian, China
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