An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis

Abstract

Background Alzheimer's disease (AD) presents a significant and escalating public health concern, with early-stage neurodegeneration often going undetected using current diagnostic procedures. Medical imaging modalities, particularly structural magnetic resonance imaging (MRI) and functional positron emission tomography (PET), provide complementary insights into the anatomical and metabolic changes associated with AD. Despite their potential, the integration of these imaging techniques into a unified, explainable artificial intelligence (AI) framework remains limited. Objectives This study aims to develop and evaluate NeuroFusion-ADNet, a novel AI model that effectively combines structural and functional imaging data to improve diagnostic accuracy and clinical interpretability in AD detection. Methods NeuroFusion-ADNet is a dual-path deep learning model that jointly processes co-registered MRI and PET slices for simultaneous region-of-interest segmentation and diagnostic classification. The model features modality-specific encoders for structural and functional feature extraction, a bi-directional cross-attention fusion layer and a segmentation-informed classification module. The framework was trained and evaluated using the Alzheimer's Disease Neuroimaging Initiative dataset, comprising 381 subjects across normal control, mild cognitive impairment) and AD categories. Performance was benchmarked against standard architectures, including ResNet152, U-Net++, and multimodal convolutional neural networks (CNNs). Recently, combining CNNs and attention mechanisms has shown highly effective results in medical image analysis. Therefore, our model integrates explainability features, including attention heatmaps and Local Interpretable Model-Agnostic Explanations. Results NeuroFusion-ADNet achieved a classification accuracy of 99.48% and a Dice coefficient of 0.985, significantly outperforming existing baselines. Attention-based visualizations confirmed that the model consistently focuses on clinically relevant brain regions such as the hippocampus, entorhinal cortex and basal ganglia. Extensive ablation studies validated the contributions of each architectural component. Conclusion This work introduces a clinically promising multimodal AI framework that enhances diagnostic accuracy while maintaining transparency through explainable techniques. NeuroFusion-ADNet sets a foundation for the development of efficient, interpretable and deployable tools in the early diagnosis of AD.