Enhancing Alzheimer'S Disease Diagnosis Via Hierarchical 3D-Fcn With Multi-Modal Features

Alzheimerƒ??s disease (AD) is an incurable, progressive neurological disorder of the human brain related to loss of memory, commonly seen in the elderly population. Accurate detection of AD can help with proper treatment and prevent brain function damage. Existing CNN-based methods need to predetermine informative locations in sMRI, which means the stage of distinguishing lesions is separated from the later stages of feature extraction and classifier construction. In this paper, a novel ``two-stage" framework based on a hierarchical 3D fully convolutional network (H-3D-FCN) is proposed to automatically identify discriminative local patches and regions in the sMRI. We further optimize the diagnosis performance by constructing a multi-layer perceptron (MLP) model which combines the multi-modal features (e.g., MMSE score, age, gender, APOE 4) with the risk probability maps (RPMs) generated from the H-3D-FCN model. Experiments on three typical AD datasets, namely, ADNI, AIBL, and NACC, show that our method achieves state-of-the-art performance as compared with recent baselines.