DAResNet-ViT: A Novel Hybrid Network for the Early Diagnosis of Alzheimer's Disease Using Multi-View sMRI and Multimodal Analysis

In diagnosing Alzheimer's Disease (AD), structural Magnetic Resonance Imaging (sMRI) is crucial, providing axial, coronal, and sagittal views for a comprehensive analysis of brain structures and pathological changes. Region of Interest (ROI) techniques in neuroimaging further allow detailed examination of structural and functional changes linked to AD. Additionally, certain SNPs have been identified as significant genetic markers for AD risk. Given the capabilities of Convolutional Neural Networks (CNNs) and Transformer models in handling multimodal data, this study introduces a novel hybrid network, DAResNet-ViT, for AD diagnosis. The model utilizes 24 key sMRI slices from each view per participant, combined with ROI features and genetic data, for classifying subjects. Data from two Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets were preprocessed and used to train and validate DAResNet-ViT. The model was evaluated through experiments focusing on AD diagnosis and the prediction of Mild Cognitive Impairment (MCI) conversion, with its effectiveness demonstrated in ablation studies and comparative experiments.