Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.

BACKGROUND/

OBJECTIVES

Alzheimer's disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI).

METHODS

Four ML classifiers-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)-were trained on demographic and clinical features. For stage-wise classification, five DL models-CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2-were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations.

RESULTS

Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement.

CONCLUSIONS

Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.