Severity Staging of Alzheimer's Disease from MRI Utilizing a Convolutional Neural Network with Alternating Pooling and SMOTEENN-Balanced Training

Alzheimer's disease (AD) is a progressive neurodegenerative condition responsible for most dementia cases globally. Diagnosing it accurately and promptly remains a significant clinical challenge due to the subtle brain structure changes in the early stages. This research introduces a framework based on a convolutional neural network (CNN) for automatically classifying the severity of AD using T1-weighted magnetic resonance imaging (MRI) scans. The model features a four-layer convolutional design with Exponential Linear Unit (ELU) activations, alternating between average and max pooling, and includes dropout regularization. To tackle the class imbalance often found in neuroimaging datasets, the Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) was utilized before training. The model underwent training for 40 epochs using stochastic gradient descent (SGD) and categorical cross-entropy loss. On a separate test set, the model achieved an accuracy of 99.87%, an AUC of 99.91%, and an F1-score of 99.86% across four severity categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. These findings indicate that the proposed architecture provides a computationally efficient and highly precise method for automated AD staging, with the potential to be integrated into clinical decision-support systems.