A Versatile Deep Learning Model for Alzheimer’s Disease Detection by Using Structural MRIs

Abstract

Alzheimer's disease (AD), a global neurodegenerative condition, primarily affects the elderly, necessitating precise and timely diagnosis for effective intervention, despite the potential for errors and time-consuming methods. Despite various techniques used for diagnosing and categorizing this disease, there is a growing requirement for improved precision in early detection. This article proposes a deep learning technique for detecting and categorizing this ailment into different categories: non-dementia, very mild, mild, and moderate dementia. It does this by employing convolution neural network(CNN) topologies. The suggested approach can be used to analyze and categorize Alzheimer's patients in real time.