# 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.  
Swin Transformer; Resizer; CNN; structural MRI; Alzheimer's disease +
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In particular, skull stripping is regarded as a crucial preprocessing step that cannot be skipped since it can lessen the influence of irrelevant data on classification outcomes and simplify the computing process of the model [9]. However, insufficient skull stripping or over-processing caused by the toolkit might result in the loss of edge information, and the data must then be manually checked after processing, which takes time and significantly reduces the already-limited dataset relevant to AD. As an improvement of the Transformer, the Swin Transformer [10] has been widely used since its introduction. Some studies have added a time-attention block to the Swin Transformer to measure the feature changes in longitudinal mild cognitive impairment (MCI) data. At the same time, the shifted-window mechanism further integrates spatial features [11]. An adaptive resize-residual network was proposed for formatting the shape and size of image data before feeding them into the backbone model, which fully excavates the CNN's powerful image feature extraction ability [12].  
In this study, we propose a deep-learning model for AD classification called the Resizer Swin Transformer (RST) using sMRI images. The Resizer module creates learnable image scaling, acquiring characteristics supporting Swin Transformer classification. The cross-window connection realized by the moving-window mechanism of the Swin Transformer and patch merging provides multi-scale learning. Additionally, a convolutional neural network (CNN) model is employed to colorize the sMRI images to enable multi-channel learning [13]. The RST can extract sufficient features and achieve the accurate classification of AD with minimal data processing.  
The rest of the contents of this paper are as follows: The RST model is briefly introduced in Section 2, along with a summary of some of the relevant work on sMRI-based categorization. The structure of the RST model is further explained in the third part. The fourth part presents the findings of the experiment together with its specifics and provides an analysis of the results. The fifth section gives an overview of all the work in this paper, suggests ways to fix the problems in this experiment, and offers a roadmap for future research.