MCA framework: a novel multi-dimensional cooperative analysis framework for Alzheimer's disease diagnosis

Abstract Structural magnetic resonance imaging (sMRI) plays a pivotal role in the diagnosis of Alzheimer's disease (AD), owing to its high-resolution whole-brain imaging capability. Recent studies have shifted from relying on prior knowledge-based extraction of sMRI features to employing deep learning approaches for automatic feature extraction. However, the deep learning-based approaches may overlook the interrelationships among features from various dimensions, which focus solely on inter-slice information or spatial structural features, thereby limiting the comprehensiveness of brain lesion characterization. To address this limitation, we propose a multi-dimensional cooperative analysis (MCA) framework designed to integrate multi-dimensional feature information effectively. The framework comprises two stages. In the first stage, multi-frequency aware (MFA) downsampling utilizes a novel multi-frequency aware fusion module based on a three-dimensional Sym2 wavelet transform to extract multi-scale features. In the second stage, the multi-view slice learner (MVSL) and the spatial structural learner (SSL), which respectively employ a multi-view encoder and a graph hybrid attention convolutional block, collaborate to capture AD lesion features from diverse perspectives. Extensive experimental validation demonstrates that the MCA framework achieves an accuracy of 95.12% and a sensitivity of 94.07% on the ADNI dataset, outperforming recent state-of-the-art methods. It also exhibits robust generalization on the AIBL and OASIS datasets. SHAP-based analysis provides interpretability to the MCA framework by revealing the differential contributions of various brain regions in AD diagnosis.