Explainable Federated Multimodal Deep Learning Framework for Early Alzheimer’s Disease Detection: Integrating MRI, Clinical Data, and Expert-Guided Few-Shot Learning with Privacy-Preserving Cross-Site Validation
Abstract
Abstract The early diagnosis of the Alzheimer disease (AD) is a critical issue in the field of healthcare, and the existing methods of diagnosis are often confined to the analysis of a single modality, inability to interpret the results, and the inability to generalize to different sites. The paper introduces an explicable federated multimodal deep learning architecture which combines structural MRI, clinical tabular data, and textual medical reports to strong detect early AD without violating the privacy of patients. We have implemented federated learning in many medical centers and a few-shot learning under the guidance of specialists to improve the issue of data scarcity and domain shift. The framework uses multimodal fusion based on cross-attention and has region-sensitive explainability using built-in XRAI (eXplanation with Ranked Area Integrals) and SHAP (SHapley Additive explanations) features. Our system is benchmarked on three datasets which are ADNI( Alzheimer’s Disease Neuroimaging Initiative ),PPMI(Parkinson’s Progression Markers Initiative) and ANMerge as a measure of external validation. The results of the experiments show a high level of performance with accuracy of 94.7 percent, sensitivity of 95.2 percent, specificity of 94.1 percent, AUC of 0.973 and F1-score of 94.9 percent outperforming the state-of-the-art centralized and single-modality by 6.3-8.7 percent. By ablation, the role of federated training ( +4.2% accuracy), multimodal fusion ( +5.8%),…
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