An efficient vision transformer for Alzheimer's disease classification using magnetic resonance images

Alzheimer's disease (AD) is the most common dementia that is often seen among the elderly. AD can cause the loss of cognitive ability and memory, which can result in death as AD is progressive. The exact cause of AD is still in research, but it is believed to be related to genes, diet, and environment. One observation of AD is the shrinkage of the hippocampus and frontal lobe cortex. Magnetic resonance imaging (MRI) is often employed in the diagnosis of AD as it can produce clear images of the soft tissues. In this study, a new computer-aided diagnosis (CAD) method named RanCom-ViT, is proposed to interpret the brain MRI slices automatically and precisely for AD diagnosis with better global representation learning and efficiency. A pre-trained vision transformer (ViT) is chosen as the backbone because ViTs with attention modules can achieve better performance than convolutional neural networks on larger datasets. Then, a novel token compression block is proposed to improve the efficiency of the RanCom-ViT by removing the less important tokens. Further, the classification head of the RanCom-ViT is enhanced by a random vector functional-link structure to obtain better classification performance in AD diagnosis. A large public brain MRI dataset is utilized in the evaluation experiments of the proposed RanCom-ViT, and it achieved an overall accuracy of 99.54% with a double throughput than the benchmark model. The results reveal that the RanCom-ViT outperforms several existing state-of-the-art AD diagnosis methods in terms of accuracy, and the token compression method contributes to higher efficiency.