Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer’s Disease

Abstract

Structural magnetic resonance imaging (sMRI) is widely used in the clinical diagnosis of diseases due to its advantages: high-definition and noninvasive visualization. Therefore, computer-aided diagnosis based on sMRI images is broadly applied in classifying Alzheimer’s disease (AD). Due to the excellent performance of the Transformer in computer vision, the Vision Transformer (ViT) has been employed for AD classification in recent years. The ViT relies on access to large datasets, while the sample size of brain imaging datasets is relatively insufficient. Moreover, the preprocessing procedures of brain sMRI images are complex and labor-intensive. To overcome the limitations mentioned above, we propose the Resizer Swin Transformer (RST), a deep-learning model that can extract information from brain sMRI images that are only briefly processed to achieve multi-scale and cross-channel features. In addition, we pre-trained our RST on a natural image dataset and obtained better performance. We achieved 99.59% and 94.01% average accuracy on the ADNI and AIBL datasets, respectively. Importantly, the RST has a sensitivity of 99.59%, a specificity of 99.58%, and a precision of 99.83% on the ADNI dataset, which are better than or comparable to state-of-the-art approaches. The experimental results prove that RST can achieve better classification performance in AD prediction compared with CNN-based and Transformer models.