Cross-Modal Intermediate Fusion of Neuroimaging and Genomic Features for Alzheimer’s Diagnosis

Recent advancements in computer-aided diagnosis of Alzheimer’s disease (AD) have primarily relied on unimodal data, particularly magnetic resonance imaging (MRI), to capture structural brain abnormalities. However, such single-source approaches often fail to account for genetic factors that play a critical role in AD onset and progression. To overcome this limitation, this study introduces an intermediate fusion architecture incorporating a cross-attention mechanism for AD classification using both MRI and genetic data. The proposed framework utilizes a ResNet-based convolutional neural network (CNN) to extract comprehensive structural representations from whole-brain MRI scans, while a Transformers encoder learns informative patterns from single nucleotide polymorphism (SNP) profiles. The features from both modalities are subsequently integrated through a cross-attention driven intermediate fusion module, enabling adaptive interaction between imaging and genetic domains to capture complementary and complex correlations. Experimental evaluations conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed model achieves an overall classification accuracy of approximately 92,94%, significantly superior to unimodal models using only MRI or SNP data. The results demonstrate the effectiveness of the proposed cross-attention-based intermediate fusion strategy in enhancing multimodal representation learning, thereby improving the reliability of early and accurate Alzheimer’s disease classification.