Early Diagnosis of Alzheimer’s Disease Based on Selective Kernel Network with Spatial Attention

Alzheimer’s disease (AD) is a neurodegenerative disorder which leads to memory and behaviour impairment. Early discovery and diagnosis can delay the progress of this disease. In this paper, we propose a new deep learning method called selective kernel network with attention for early diagnosis of AD using magnetic resonance imaging. Generally, deep learning methods for high-accuracy recognition are based on structure of deep neural networks by stacking a myriad of convolutional layers in the model. In this paper, the structure of SKANet is constructed similarly to that of ResNeXt by repeating residual blocks with the same topology and group convolution for saving computational costs. Different from ResNeXt, the primary convolution is replaced by using selective kernel convolution to adaptively adjust the receptive field based on imported information. Then, attention mechanism is added to the bottom of the block to emphasize on important features and suppress unnecessary ones for more accurate representation of the network. The block is termed as selective kernel with attention block that consists of a sequence of operations followed by the order: a convolution with kernel size \(1\times 1\), a selective kernel convolution, a convolution with kernel size \(1\times 1\), and spatial attention mechanism. The effectiveness of this proposed model is verified based on the Alzheimer’s Disease Neuroimaging Initiative dataset. Our experimental results show superiority of the proposed model for the early diagnosis of AD. The classification accuracy of AD and mild cognitive impairment reaches up to \(98.82\%\).