Vision Transformer Approach for Classification of Alzheimer’s Disease Using 18F-Florbetaben Brain Images

Abstract

Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images. The convolutional neural network (CNN) is a representative application of deep learning, serving as a powerful tool for the diagnosis of AD. Recently, vision transformers (ViT) have yielded classification performance exceeding that of CNN in some diagnostic image classifications. Because the brain is a very complex network with interrelated regions, ViT, which captures direct relationships between images, may be more effective for brain image analysis than CNN. Therefore, we propose a method for classifying dementia images by applying 18F-Florbetaben positron emission tomography (PET) images to ViT. Data were evaluated via binary (normal control and abnormal) and ternary (healthy control, mild cognitive impairment, and AD) classification. In a performance comparison with the CNN, VGG19 was selected as the comparison model. Consequently, ViT yielded more effective performance than VGG19 in binary classification. However, in ternary classification, the performance of ViT cannot be considered excellent. These results show that it is hard to argue that the ViT model is better at AD classification than the CNN model.

Understanding the brain with attention: A survey of transformers in brain sciences

“…As illustrated in Figure 4, ViT has also become an important baseline for brain disease diagnosis. For example, Odusami and Shin et al [71,73] . carried out the diagnosis of Alzheimer's Disease (AD) using a fine‐tuned ViT.…”

“…brain disease diagnosis. For example, Odusami and Shin et al [71,73] carried out the diagnosis of Alzheimer's Disease (AD) using a fine-tuned ViT. They focused on exploring the prediction of AD and amyloid changes via Positron Emission Tomography (PET), since AD biomarkers are an important reference for no or Mild Cognitive Impairment (MCI).…”

“…[90] Odusami et al [71] fused MRI with [18F]-Fluo-roDeoxyGlucose (FDG) PET [91,92] for richer semantics. Shin et al [73] were the first to apply the ViT to the [18F]-Flor-BetaBen (FBB) PET dataset. [93] Tummala et al [74] exploited the fine-tuned ViT in the diagnosis of brain tumors, which offered support to radiologists for decision-making.…”

“…fused MRI with [18F]‐FluoroDeoxyGlucose (FDG) PET [91,92] for richer semantics. Shin et al [73] . were the first to apply the ViT to the [18F]‐FlorBetaBen (FBB) PET dataset [93] .…”

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG

“…Although several studies have demonstrated the potential of Transformers for the classification of AD and its transitional stages using MRI [38]- [41] and PET images [42], [43], a very limited number of works have employed these models with EEG data [44]- [46] and none of them has investigated the potential of attention scores as interpretability tools to find pathological biomarkers of AD.…”