Alzheimer's Disease (AD) remains the leading cause of dementia worldwide. It gradually progresses from mild -severe, limiting one's capacity to do any task without help. It begins to outpace owing to population ageing and the diagnostic schedule. It has a significant negative impact on affected individuals and their quality of life. An early diagnosis can help them manage their healthcare demands much more effectively. In the past few years, there has been an increased focus on the development of automated approaches for the identification of different illnesses, leveraging advances in artificial intelligence. This study focuses on Alzheimer’s disease detection, which combines U-Net for segmentation and CNNs for classification, has the potential to significantly advance Alzheimer's disease diagnostics. ADNI dataset is used in this study and the model achieves an accuracy rate of 93% after the process of pre-processing and segmentation.

Alzheimer's Disease U-Net CNN Image pre-processing Performance Metrices