Multimodal Deep Learning for Early Detection of Alzheimer's Disease: An Interpretable AI Solution Fusing Imaging and Clinical Data

This article introduces an early detection system for Alzheimer's disease based on multimodal deep learning, fusing MRI brain imaging and clinical data. It uses VGG16, ResNet50, and MLP models for late fusion, and provides interpretable prediction visualization via Grad-CAM technology to help doctors understand the basis of model decisions. This project offers an end-to-end reference implementation for early AD detection, embodying the concept of AI-assisted healthcare.

Alzheimer's Disease (AD) is the most common cause of senile dementia; 60-70% of the approximately 55 million dementia patients worldwide are diagnosed with AD. Early detection is crucial for delaying disease progression, but traditional diagnosis relies on experience-based judgments which are highly subjective. This project uses medical imaging and AI technology to build an auxiliary diagnosis system, aiming to solve the problem of early detection.

The project adopts a multimodal fusion strategy, combining MRI structural information and clinical data semantic information. The models include VGG16 (for extracting imaging features), ResNet50 (for deep feature representation), and MLP (for processing clinical data). Late fusion (concatenating features extracted independently from each modality) is used to improve classification accuracy and robustness.

To address the AI black-box problem, the project introduces Grad-CAM to generate heatmaps, indicating the regions in the images that contribute the most to predictions. Its clinical value includes verifying model-focused regions, assisting doctors in diagnosis, enhancing patient trust, and discovering potential biomarkers.

The project is developed using Jupyter Notebook. Data preprocessing includes image skull stripping, registration, and enhancement, as well as clinical data encoding. The training strategy uses the Adam optimizer, cross-entropy loss, regularization, and early stopping. Evaluation metrics include accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix.

Current challenges include difficulty in data acquisition, complex modality alignment, high computational resource requirements, and unvalidated generalization ability. Future directions include introducing attention mechanisms, fusing more modalities, longitudinal analysis, federated learning, and lightweight deployment.

The core contributions of the project are the multimodal fusion architecture, interpretable AI practice, and complete engineering implementation. It provides a reference for medical AI researchers, embodies the concept of "AI assisting rather than replacing doctors", helps improve diagnostic accuracy, and benefits patients.