Multi-scale attention-based pseudo-3D convolution neural network for Alzheimer's disease diagnosis using structural MRI

Recently, deep learning based Computer-Aided Diagnosis methods have been widely utilized due to their highly effective diagnosis of patients. Although Convolutional Neural Networks (CNNs) are capable of extracting the latent structural characteristics of dementia and of capturing the changes of brain anatomy in Magnetic Resonance Imaging (MRI) scans, the high-dimensional input to a deep CNN usually makes the network difficult to train, and affects its diagnostic accuracy. In this paper, a novel method called the hierarchical pseudo-3D convolution neural network based on a kernel attention mechanism with a new global context block, which is abbreviated as “PKG-Net”, is proposed to accurately predict Alzheimer's disease even when the input features are complex. Specifically, the proposed network first extracts multi-scale features from pre-processed images. Second, the attention mechanism and global context blocks are applied to combine features from different layers to hierarchically transform the MRI into more compact high-level features. Then, a joint loss function is used to train the proposed network to generate more distinguishing features, which improve the generalization performance of the network. In addition, we combine our method with different architectures. Extensive experiments are conducted to analyze the performance of the PKG-Net with different hyper-parameters and architectures. Finally, in order to verify the effectiveness of our method on Alzheimer's disease diagnosis, we carry out extensive experiments on the ADNI dataset, and compare the results of our method with that of existing methods in terms of accuracy, recall and precision. Furthermore, our network can fully take advantage of the deep 3D convolutional neural network for automatic feature extraction and representation, and thus can avoid the limitation of low processing efficiency caused by the preprocessing procedure in which a specific area needs to be annotated in advance. Finally, we evaluate our proposed framework using two public datasets, ADNI-1 and ADNI-2, and the experimental results show that our proposed framework can achieve superior performance over state-of-the-art approaches.