MADF-Net: A Multi-Branch Adaptive Deep Fusion Network for Alzheimer's Disease Prediction

Ning Wang, Pingan Tian, Minghui Wu, Haojie Zhou

Abstract: Alzheimer's disease (AD) is one of the key diseases that seriously threatens the health of the elderly population and significantly increases the burden of social elderly care. The widespread application of artificial intelligence technology in the medical field has brought new ideas for the early diagnosis and intervention of AD. To this end, we propose a novel Multi-Branch Adaptive Deep Fusion Network (MADF-Net) to improve the accuracy of AD diagnosis. The MADF-Net consists of three main parts: the Supervised Autoencoder Alignment Module (S-AEAM), the Multi-Branch Adaptive Fusion Module (MAFusion), and the Dynamic Gated Deep Fusion Decision Module (DGD-Fusion). The S-AEAM is used to align and enhance the features of different modalities. The MAFusion aims to retain the main information of the current modality while integrating features from other modalities. Finally, the DGD-Fusion is employed to deeply fuse the features from all modalities and obtain the final output. Extensive experiments conducted on the Cookie Theft corpus from DementiaBank demonstrate that our proposed MADF-Net outperforms state-of-the-art (SOTA) models, achieving an accuracy of 88.91% and an F1 score of 90.48%.

External IDs:dblp:journals/spl/WangTWZ25