Search

In recent years, deep learning has emerged as a promising framework for identifying Alzheimer's disease (AD) within clinical neuroimaging. Nonetheless, conventional architectures frequently fail to optimize feature extraction from medical scans and underutilize auxiliary data resources vital for diagnostic categorization. To resolve these challenges, this paper introduces a deep three-dimensional convolutional neural network architecture that integrates attention mechanisms with multi-task learning. An enhanced primary C3D network is deployed to generate initial, coarse low-level feature maps. This design implements a novel convolutional block tailored to capture the specific structural properties of magnetic resonance imaging (MRI) data, along with a secondary block configured to compute localized attention weights for distinct pixel coordinates within the feature map, which are subsequently combined with the map's output. Thereafter, a series of fully connected layers implements multi-task learning, producing three distinct outputs that encompass the primary classification objective. During the training phase, the two auxiliary outputs leverage backpropagation to refine and enhance the primary classification performance. Empirical results demonstrate that this proposed framework outperforms existing state-of-the-art models for AD detection, yielding superior classification accuracy and enhanced performance metrics on the Alzheimer's disease Neuroimaging Initiative dataset. These outcomes illustrate a compelling direction for prospective diagnostic classification research.