Early Detection of Alzheimer's Disease Using Variational Autoencoders on MRI Scans

Abstract

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that impairs memory, cognition, and daily functioning, predominantly affecting elderly individuals. Early detection of AD remains challenging due to the reliance on manual MRI analysis and clinical assessments, which are often time-consuming and prone to errors. This project aims to develop an automated and accurate system for early detection of Alzheimer’s disease using Generative Artificial Intelligence. The proposed approach leverages Variational Autoencoders (VAEs) to learn meaningful latent representations from MRI brain scans, facilitating effective classification of different Alzheimer’s stages. The goal is to enhance diagnostic accuracy and support continuous patient monitoring by analyzing the progression of the disease over time. Deep learning techniques are employed to uncover subtle patterns that traditional methods may overlook. The system is implemented in Python using deep learning frameworks such as TensorFlow/Keras, along with OpenCV for image processing.