Variational Autoencoder-Based Early Detection System for Alzheimer’s Disease from MRI Scans

Abstract

Alzheimer’s disease is a chronic and progressive neurodegenerative disorder that primarily affects memory, cognitive abilities, and daily functioning. Early diagnosis of Alzheimer’s disease is critical for effective treatment planning and slowing disease progression. Traditional diagnostic methods rely heavily on clinical evaluations, cognitive tests, and patient history, which are often subjective and may lead to delayed diagnosis. Recent advancements in deep learning have enabled automated and objective analysis of medical imaging data. In this paper, a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Variational Autoencoders (VAEs) is proposed for early Alzheimer’s disease detection using MRI scans. CNNs are utilized to extract high-level spatial and structural features from brain images. VAEs are employed to learn compact latent representations that capture subtle anatomical changes in brain tissues. The extracted features are fused and used for classification into Alzheimer’s and healthy control categories. The proposed model is trained and evaluated on a dataset of approximately 500 MRI images. Performance is analyzed using accuracy, sensitivity, specificity, and confusion matrix metrics. Experimental results demonstrate improved diagnostic performance compared to conventional approaches. The proposed framework offers a reliable and automated tool for early Alzheimer’s detection. This approach contributes to precision medicine and supports clinical decision-making. The results indicate strong potential for real-world medical applications.