Detecting different stages of Alzheimer's disease from MRI images using deep learning and computer vision techniques

Abstract

The precise and early detection of Alzheimer's disease (AD) is essential for prompt intervention and effective management. Neuroimaging methods, such as magnetic resonance imaging (MRI), offer crucial information about brain structure and function; however, they can be time-consuming and prone to observer variability when interpreted manually. This study proposes a novel deep learning framework that leverages computer vision techniques to automate the classification of AD stages, including early & late Mild Cognitive Impairment (MCI), using MRI images. A custom Convolutional Neural Network (CNN) model was developed and optimized, achieving a classification accuracy of 96.6%, surpassing state-of-the-art architectures. To address challenges like class imbalance and data scarcity, we employed advanced data augmentation techniques and unsupervised pre-training using a denoising convolutional autoencoder. Additionally, model explainability was improved through gradient-weighted class activation mapping (Grad-CAM), highlighting critical parts in MRI scans that influence predictions. The findings show that this method could help enhance diagnostic accuracy and provide a robust tool for neurodegenerative disease research and clinical application.