## 1 Introduction
### Related Works  
Due to the importance of prevention and delay, there are many studies on the diagnostic classification of AD. For example, [PERSON] et al. [26] proposed a VGG-TSwinformer model based on a convolutional neural network (CNN) and transformer. The classification process of stable MCI (sMCI) and progressive MCI (pMCI) was performed, and an accuracy of 77.2%, sensitivity of 79.97%, specificity of 71.59%, and AUC of 0.8153 were obtained. The VGG-TSwinformer model is a deep learning model for short-term longitudinal studies of MCI that can build a model of brain atrophy progression from longitudinal MRI images and improve diagnostic efficiency compared to algorithms that only use cross-sectional sMRI images. [PERSON] et al. [19] proposed a SMIL-DeiT network for the AD classification task between three groups: AD, MCI, and normal control (NC). Vision Transformer is the basic structure of our work, and the data pre-training was performed using DINO, a self-supervised technique, while the downstream classification task was performed by multi-instance learning. The learning performance reached 93.2% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (MRI), with the accuracy higher than 90.1% of Transformer and 90.8% of CNN. [PERSON] et al. [27] studied three deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transducer-based architectures (MAE and DeiT) to improve the automatic detection of dementia in MRI brain images. Experiments showed that the very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. The significant improvement in accuracy (up to 7%) motivated us to consider the CAD approach in real-world applications. [PERSON] et al. [17] proposed a slicewise convolutional embedding method to improve the standard patching operation in vanilla ViT. The proposed cross-domain transfer learning method classified AD and CN, with an accuracy of 95.3%, recall of 94.4%, and precision of 90.0%, which can achieve similar classification performance compared to the most recent research. [PERSON] et al. [28] proposed a multimodal method based on MRI and PET modalities for the diagnosis of [PERSON]'s disease using a combination of efficientnet V2 and a vision converter enhanced by a novel data augmentation based on self attention generative adversarial networks (SAGAN). The proposed method achieved 96% accuracy by combining the main advantages of vision transducer and Efficientnet V2. We validated the proposed method using ADNI and the Open Access Series of Imaging Studies (OASIS). [PERSON] et al. [29] proposed a three-dimensional medical image classifier using Multi-plane and Multi-slice Transformer (M3T) networks to classify Alzheimer's disease in three-dimensional MRI images. They used the Alzheimer's Disease Neuroimaging Initiative (ADNI) training dataset containing MRI images, and for validation data, they used datasets from three institutions (AIBL, OASIS, and ADNI). In the validation results, ADNI achieved AUC 0.9634 and ACC 93.21%, AIBL with AUC 0.9258 and ACC 93.27%, and OASIS with 0.8961 and ACC 85.26%, which demonstrated the feasibility of efficiently combining CNN and Transformer for 3D medical imaging. [PERSON] et al. [30] analyzed the performance of a multi-visual transducer network to detect AD based on features extracted from a set of 2D coronal slices. ImageNet was used to train the model with coronal 2D slices, which were selected to utilize transfer learning properties. The classification performance to distinguish between AD and CN showed an ACC of 88.2%, a recall of 95.6%, and a specificity of 77.4%. [PERSON] et al. [31] proposed an advanced deep learning architecture called Brain Informer (Bralnf) based on an efficient self-attention mechanism. The proposed model integrated representation learning, feature extraction, and classifier modeling into a unified framework. The effectiveness of the proposed model was validated using the Alzheimer's Disease Neuroimaging Initiative dataset.