## 1 Introduction  
Dementia is a degenerative disease that is increasing in prevalence within an aging population [1]. Alzheimer's disease (AD) is the most common type of dementia, accounting for 60-80% of dementia cases, and is one of the leading causes of death worldwide [2]. AD begins with mild declines in memory, thinking, and learning processes and may lead to severe loss of consciousness and difficulty with physical abilities due to brain damage [3]. Although it is possible to prevent and delay the onset of AD using FDA-approved therapeutic approaches, there is currently no treatment that can dramatically reverse the pathological changes following onset [4]. Therefore, early detection and management are the best ways to slow the progression of AD, and early diagnosis is especially crucial. AD biomarkers (biological markers of diseases such as amyloid and tau) can be utilized for the early identification of diseases in people with mild or no cognitive impairment [5]. Amyloid accumulation in the brain, which is one of the causes of AD, is known to occur when an abnormal form of amyloid is deposited in the brain due to a metabolic problem [6]. Through an amyloid positron emission tomography (PET) test, an amyloid biomarker is injected into the body, and a brain image is taken to determine the location and amount of the deposited amyloid. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images [7; 8; 9]. The convolutional neural network (CNN), a representative application of deep learning, is primarily used in image data recognition tasks, and has also been employed as a powerful tool in diagnosing AD [10; 11; 12]. Almost all previous studies have used CNN-based backbones for feature extraction; however, vision transformers (ViT) have recently emerged as an image diagnostic classification tool to replace CNNs. The transformer architecture was initially developed for the natural language processing (NLP) field [13]. It has since been applied to computer vision tasks [14; 15; 16], as it yields a performance exceeding that of CNN in some diagnostic image classifications [17; 18; 19]. Whereas a CNN uses a convolutional filter to gradually extract important features from an entire image, a ViT directly captures the correlation between image areas. Because the brain is a very complex network with interrelated regions, vision transformers that capture direct relationships between images may be more effective for brain image analysis than CNN. Many prior studies have conducted AD classification by applying FBB PET images to various CNN models; however, such images have not yet been used for ViT models. The most common type of data used with ViT is MRI data [17; 18; 19; 20; 21; 22; 23], with 18F FDG images used as PET images [24; 25]. We therefore applied a novel 18F-FBB PET image to a ViT, and subsequently evaluated the classification performance. VGG19 [24] was selected for comparison with ViT, with both models being pre-trained using ImageNet. The performance of the two pre-trained models was evaluated through a two-group classification (normal control and abnormal) and three-group classification (healthy control, mild cognitive impairment, and Alzheimer's disease). This article makes several contributions. First, it was confirmed that the pretrained model and augmented data effectively classify brain PET image data into brain imaging regions. Lack of medical image data is a chronic problem in learning models, and this study utilized data augmentation to solve the imbalance of each class among the data. We expected that the learning performance would improve considering the non-generalization of the model when training an insufficient amount of brain PET images as data, but rather, when the model is trained by augmenting data with severely imbalanced data of each class, we found that the learning performance was worse.