Alzheimer’s Disease and Mild Cognitive Impairment Detection Using sMRI With Efficient Receptive Field and Enhanced Multi-Axis Attention Fusion

Deep neural networks have shown promising results in the analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, particularly Alzheimer’s disease (AD). Different regions of the brain have diverse structures that are linked to specific functions, which could account for the variability in disease-related changes observed in sMRI scans of these areas. Understanding the overall characteristics of sMRI data is important since current popular convolutional neural networks (CNN) for deep learning do not consider the interconnection of voxels. Vision transformers have shown effectiveness in identifying long-distance connections in the brain, which has led to their success in applications such as disease detection. However, the image noise and limited scalability of self-attention mechanisms in relation to image size has hindered their widespread use in advanced Alzheimer’s analysis. To enhance information retention and reduce network complexity, this study presents a novel adaptable efficient receptive field feature extraction network. Moreover, an advanced attention mechanism with both local grid attention block and dilated global attention module has been incorporated to highlight the characteristics of AD. Next, a more improved hierarchical inverted residual feed forward network in place of multi-layer perceptron is suggested to enhance the characterization of features through the integration of information from both lower and higher layers. Finally, the global average pooling and $1\times 1$ convolution are used to reduce dimensionality, enhance non-linearity, and allow channel interactions in feature maps before being input into the classification head. The network achieved high performance in various scenarios, with average accuracies of 97.29% for AD vs. HC and 94.79% for MCI vs. HC classification using ADNI as experimental datasets.