Classification of Alzheimer’s Disease by Magnetic Resonance Imaging with an Ensemble Approach

• Gustavo S. Silva UEMA
• Omar A. C. Cortes IFMA / UEMA
• Antonio F. L. Jacob Jr. UEMA

DOI: https://doi.org/10.5753/sbcas.2025.6919

Resumo

Alzheimer’s Disease (AD), affecting over 55 million people globally, demands reliable diagnostic tools. Single-model approaches using CNNs and traditional ML face critical limitations. This study proposes two frameworks: a stacking-CNN ensemble (VGG-16, ResNet-101, DenseNet-121) and two voting ML ensembles (Voting[all]: KNN, RF, SVC, LR, XGBoost; Voting[few]: KNN, RF, XGBoost). Evaluated on 6,400 MRIs, Voting[few] achieved the highest classification metrics (97.8% accuracy; 0.984 MCC; 93.8% F1macro), outperforming individual CNNs, validated through Friedman-Nemenyi tests. Results suggest, in this context, that simpler ML models might better capture the inherent characteristics of MRI data for AD diagnosis.

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