GGDA-net: geometry-guided deformable attention network for Alzheimer’s disease image classification

BackgroundConvolutional neural networks (CNNs) have achieved remarkable success in medical image analysis, including Alzheimer’s disease (AD) classification. However, conventional convolution operations rely on fixed sampling patterns, and most existing attention mechanisms primarily focus on feature responses while neglecting spatial sampling geometry, limiting their ability to capture structural variations in brain images.MethodsTo address these limitations, this paper proposes a Geometry-Guid