Improved Classification of Alzheimer’s Disease With Convolutional Neural Networks

This work focuses on classifying MRI images using machine learning models to identify Alzheimer’s disease (AD), the most common form of dementia, at an early stage [1]. It is now possible to identify and forecast the onset of AD by analyzing brain scans collected through Magnetic Resonance Imaging (MRI) and using artificial intelligence (AI) technologies, classifying patients as either at risk or not. The main goal is to make precise predictions. Improved prediction and detection tools for radiologists, physicians, and caregivers will be made available as a result of the study’s determination of the likelihood that individuals would develop AD and proper categorization of them. Using machine learning methods such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and fastai—a user-friendly deep learning library and framework—we developed models using a dataset of 6400 MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 4 class for the early detection and classification of AD [2]. The models achieve an impressive accuracy rate of 84.4%, with the addition of a pre-trained Visual Geometry Group (VGG) layer to the sequential model, outperforming other algorithms. This study demonstrates a viable method for classifying and diagnosing Alzheimer’s disease early on, utilizing MRI images and machine learning models. The acquired accuracy rates show the models’ potential to assist doctors and other caregivers in AD diagnosis and management, thereby maximizing overall effectiveness.