Multi-modal Multi-Level Neuroimaging Fusion with Modality-Aware Mask-Guided Attention and Deep Canonical Correlation Analysis to Improve Dementia Risk Prediction

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by structural and molecular changes in the brain. Early diagnosis and accurate subtyping are essential for timely intervention and therapeutic planning. This thesis presents a novel multimodal deep learning framework that integrates T1-weighted MRI and Amyloid PET imaging to improve the diagnosis and stratification of AD. The proposed architecture leverages a two-stage pipeline involving modality-specific feature extraction using ResNet50 backbones, followed by middle fusion enhanced with a Modality-Aware Mask-Guided Attention (MAMGA) mechanism. To address missing modalities and inter-modal misalignment, the model incorporates Random Modality Masking and Deep Canonical Correlation Analysis (DCCA) for cross-modal feature alignment. Experiments on the ADNI dataset demonstrate that the proposed MRI+PET (MAMGA+DCCA) model achieves a balanced accuracy of 0.998 and an AUC-ROC of 0.999 in distinguishing stable normal cognition (sNC) from stable Alzheimer's Disease (sDAT). For the more challenging task of separating stable and progressive MCI (sMCI vs. pMCI), the best-performing fusion model achieved a balanced accuracy of 0.732 and an AUC of 0.789. Extensive ablation studies confirm the contributions of MAMGA, DCCA, and dual-optimizer strategies in enhancing diagnostic robustness. This work highlights the clinical potential of multimodal deep learning frameworks in improving early Alzheimer's detection and stratification.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Singh, Swapnil Satyendra

Chairs dc:contributor.committeechair

• Zhang, Liqing
• Da, Ma

Committee members dc:contributor.committeemember

• Thomas, Christopher Lee
• Murali, T. M.

• Alzheimer's Disease
• Deep Learning
• DCCA
• Early Detection
• Modality Fusion
• MRI
• Multimodal Fusion
• PET

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:44257

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135490