Recognition of Alzheimer's Disease on sMRI based on 3D Multi-Scale CNN Features and a Gated Recurrent Fusion Unit

Accurate diagnosis of Alzheimer's Disease (AD) is still a public health challenge, and has been studied for several years now to make it efficient and more automatic. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on 3D Multi-scale Feature (3DMF) blocks and Gated Recurrent Fusion Unit (GRFU). Hippocampal Volumes Of Interest (VOI) are used as input. The method is applied on sMRI imaging modality standard for patients screening. First, multiscale features are extracted via 3D Convolutional Neural Network (CNN). They are then taken as input to Gated Recurrent Units (GRU) for performance improvement. Extensive experiments are performed on the public Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. The experimental results demonstrate that our 3DMF model combined with GRFU obtains the state-of-the-art performance compared with the existing conventional methods. Furthermore, proposed approach yields a significant enhancement in terms of avoiding high similarity between classes and overfitting issue. Hence, our proposed CAD has the potential to significantly improve the conventional recognition and classification strategies for use in clinical applications.

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