Alzheimer’s Disease Detection Using Vision Transformers: A survey - Asem AL-Sultani, Nasrallah; Taima Albu-Salih, Alaa; Majeed Hilal, Osama (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2025)
Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects individuals aged 65 and older, leading to irreversible memory loss and cognitive decline. Early detection plays a critical role in managing the disease and improving patient outcomes. In recent years, numerous studies have investigated the development of automated systems to identify the stages of Alzheimer's disease using advanced deep learning methods. This paper provides a structured literature review focused on the use of Vision Transformers (ViTs) and metaheuristic optimization algorithms for early diagnosis. The reviewed studies demonstrate that ViT-based models outperform traditional approaches in extracting spatial and temporal features from brain imaging data, achieving classification accuracies exceeding 96% on widely used datasets such as ADNI and OASIS. Additionally, the review addresses key challenges in processing 3D medical images and highlights ongoing efforts to develop hybrid architectures that integrate the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT). The paper also explores how collaborative learning strategies can enhance model training while preserving patient privacy, making these techniques more suitable for real-world clinical applications.