An Explainable Hybrid Multimodal Model for Alzheimer's Disease Detection

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a major global health concern. Early and accurate prediction of AD stages, particularly during the Early and Late Mild Cognitive Impairment (EMCI, LMCI), is crucial for timely intervention. While deep learning (DL) models have shown promise, most prior work relies on single-data modality, leading to limited diagnostic accuracy. This work presents a novel multimodal DL model that integrates neuroimage and tabular clinical data to improve AD detection. Trained and tested on the OASIS dataset, the proposed model combines the extracted embeddings from the image data through a dense network with selected clinical features, identified via SHAP-based feature attribution and cumulative contribution thresholding. This integration enables a four-way classification across Normal Cognition (NC), EMCI, LMCI, and AD that surpasses the state-of-the-art performance with a precision of 96.02%, a recall of 95.84%, and an F1 score of 95.92%, alongside an overall accuracy of 95.84%.