A novel dual-branch Alzheimer’s disease diagnostic model based on distinguishing atrophic patch localization

Magnetic resonance imaging (MRI), a non-ionizing radiation imaging method, is widely utilized in diagnosing Alzheimer’s disease (AD) due to its excellent imaging ability for brain soft tissues and high-resolution slice imaging. However, the enormous size of 3D MRI makes it difficult to process and analyze it. Therefore, a challenge is to accurately mine encephalatrophy patches from 3D MRI and build a patch-based diagnostic model. The existing patch-based methods mainly extract and fuse features of each patch in isolation, ignoring mutual information between patches, which makes the diagnosis performance unsatisfactory. We propose a novel dual-branch AD diagnostic model based on distinguishing atrophic patch localization. (1) We propose a Distinguishing Atrophic Patch Localization (DAPL) algorithm based on Distinguishing Index (DI) and Spatial Contact Ratio (SCR) to extract lesion areas that have significant impacts on diagnosis from 3D MRI. Meanwhile, we proposed a Discontinuity-voxel-based Dynamic Voxel Wrapping algorithm (DV \(^2\) ${}^2$ W) to calculate DI for each patch. (2) A dual-branch diagnostic network (DBDN) is constructed to obtain intra-patch and inter-patch features synchronously. The intra-feature extraction branch extracts feature from each patch through a parallel multi-channel network and fuse them. The Inter-feature extraction branch defines the Spatial Context Mixing matrix (SCM) and performs feature extraction on SCM to obtain mutual information. The performance evaluation demonstrates that our DBDN model has adequate diagnostic performance compared to state-of-the-art methods. In addition, the distinguishing pathological locations identified by our DAPL algorithm can effectively guide inexperienced clinical doctors to identify lesion areas and guide doctors in diagnosis quickly.