Abstract
Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which comprised 2,248 3D MRI images and diverse patient demographics, the proposed model achieved an accuracy of 92.14%, a precision of 86.84%, a sensitivity of 93.27%, and a specificity of 89.95% in distinguishing between AD, healthy controls (HC), and moderate cognitive impairment (MCI). The findings suggest that VECNN can be a valuable tool in clinical settings, providing a non-invasive, cost-effective, and objective diagnostic technique. This research opens the door for future advancements in early diagnosis and personalized therapy for Alzheimer's Disease.