双模态迭代交叉注意力融合集成框架OA

Alzheimer's disease(AD),as a progressive neurodegenerative disorder,presents significant challenges in early diagnosis and clinical intervention.In medical imaging,structural magnetic resonance imaging(sMRI)captures brain atrophy and structural alterations through high-resolution anatomical imaging,while fluorodeoxyglucose positron emission tomography(FDG-PET)effectively reflects functional changes by monitoring cerebral glucose metabolism.These two modalities hold complementary value in detecting AD-related pathological brain changes.However,existing multimodal AD classification models are limited by suboptimal feature fusion,insufficient inter-modal information interaction,and feature distribution discrepancies,hindering their diagnostic utility.To address these issues,a bimodal iterative cross-attention fusion ensemble framework(BICAFEF)is proposed.This framework comprises base classifiers and a meta-classifier.The base classifiers employ ResNet modules to extract features from sMRI and FDG-PET image patches.A spatial feature shrinking(SFS)module,integrating convolutional operations and adaptive aggregation pooling,is designed to reduce inter-modal redundancy and emphasize discriminative features.Additionally,an iterative cross-attention mechanism is constructed to dynamically capture and reinforce global dependencies and complementary information across modalities through multi-round iterations,thereby resolving the challenge of insufficiently exploiting inter-modal synergies and enhancing AD classification performance.To further improve whole-brain classification accuracy,the framework incorporates a meta-classifier to screen and ensemble base classifiers by discarding those with accuracy below 75％,retaining high-performance classifiers to boost robustness and precision.Visualization analyses validate the framework's focus on critical brain regions,demonstrating its capability to effectively identify AD-related pathological areas in sMRI and PET modalities.Experimental results show that the framework achieves a five-fold classification accuracy(ACC)of 94.3％,sensitivity(SEN)of 92.6％,specificity(SPE)of 96.3％,AUC of 97.5％,and Matthews correlation coefficient(MCC)of 88.7％in AD vs.healthy control(HC)classification,outperforming state-of-the-art multimodal frameworks.