Yuxia Li ^1* Guanqun Chen ² Guoxin Wang ³ Zhiyi Zhou ^4* Shan An ^4* Chao Zhang ^4* Yuxin Jin ^4* Mingkai Zhang ^5* Feng Yu ^3*

• ¹ Tangshan Central Hospital, Tangshan, Hebei Province, China
• ² Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
• ³ Zhejiang University, Hangzhou, Zhejiang Province, China
• ⁴ JD Health International, Beijing, China
• ⁵ Xuanwu Hospital, Capital Medical University, Beijing, Beijing Municipality, China

Background Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. Methods In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN).On this dataset, we conduct binary classification experiments of AD vs CN and MCI vs CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients'