Optimized CNN Ensemble with Class-Balanced MRI Data Augmentation for Accurate Multi-Class Dementia Diagnosis

Dementia is a progressive neurodegenerative disorder that significantly impacts cognitive function, with early and accurate diagnosis remaining a clinical challenge. Traditional diagnostic methods relying on manual interpretation of neuroimaging data are not only time-consuming but also subject to variability and delayed intervention. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have shown promise in automating dementia diagnosis using brain MRI data. However, most existing approaches are limited to binary classification, lack robustness in handling imbalanced datasets, and often neglect the clinical nuances of distinguishing multiple dementia stages. In this study, we propose an optimized CNN ensemble model that combines EfficientNetB0 and ResNet50 architectures, enhanced with class-balanced data augmentation and a soft voting mechanism to improve classification reliability across three dementia stages: Non-Demented, Mild Demented, and Moderate Demented. The ensemble incorporates advanced training strategies, including dropout regularization, early stopping, and adaptive learning rates, to ensure high generalization and reduce overfitting. Feature attention mechanisms are integrated to focus on the most discriminative regions in T1-weighted brain MRI scans. Experimental evaluation on a curated subset of the ADNI dataset, consisting of 6420 MRI images, demonstrates that our model achieves superior performance, attaining an overall accuracy of 99%, macro-average F1-score of 0.99, and AUC of 1.00 across all classes. The model also exhibits high confidence and low variance in its predictions, particularly excelling in the accurate identification of moderate dementia cases, a traditionally underrepresented and harder-to-detect category. These results affirm the efficacy of combining ensemble CNN architectures with targeted data balancing strategies for robust, multi-class dementia classification. Our findings underscore the potential of deep learning-driven diagnostic tools to support early-stage dementia detection and progression monitoring in clinical settings.