AI-Based Techniques for Classifying Abnormalities Linked to Alzheimer's Disease Progression

Abstract

Alzheimer's disease (AD) is a brain disorder that gets worse over time and makes diagnosis and treatment much harder. It is very important to find problems early and correctly diagnose them in order to use successful treatment plans. Recently, artificial intelligence (AI), as deep learning (DL) methods, has shown that they could help doctors diagnose and classify problems more accurately in people with Alzheimer's disease. This essay explores how different AI techniques can be used to find and group neuropathological changes that are typical of Alzheimer's disease. Our method uses cutting-edge AI tools like convolutional neural networks (CNNs), recurrent neural networks (RNNs), Residual Networks (ResNet), and MobileNet. These all play important roles in processing and analysing brain images and clinical data. We do a thorough analysis of the current state of AI uses in the imaging and clinical data analysis of AD. We focus on how these models help tell the difference between normal ageing and the early stages of Alzheimer's, as well as how they can be used to stage the disease. Our results clearly show that MobileNet does a better job than other models at classifying problems related to AD. This is because it is better at working with big sets of images. In addition, we talk about how combining different types of data sources makes monitoring tools much more accurate and reliable. It also talks about the problems and moral issues that come up when using AI in hospital settings. This shows how AI has the ability to completely change the way diagnoses are made. AI has the potential to completely change Alzheimer's study because it improves the accuracy of diagnoses and helps make focused treatments..