Integrated MRI-Cognitive Analysis Platform for Early Alzheimer's Diagnosis and Personalized Care

Alzheimer’s disease (AD) gradually impairs mem- ory, cognition, and functional independence, representing one of the most pressing neurological health challenges globally. In this paper, we present a multi-modal diagnostic framework that combines deep learning-based MRI analysis with standardized cognitive assessment scoring to produce a fused severity index for AD stage classification. Our system classifies patients into four stages: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. We evaluate the framework on clinical case data, demonstrating 100% MRI classification confidence on representative cases. The cognitive scoring module implements a Mini-Mental State Examination (MMSE)-style assessment across seven cognitive domains. A weighted fusion mechanism inte- grates both modalities into a unified severity index on a 0–100 scale. We present a detailed case study and provide clinically actionable recommendations spanning diet, lifestyle, exercise, and medical intervention. Our results suggest that multi-modal fusion significantly enhances diagnostic reliability compared to single- modality approaches. Index Terms—Alzheimer’s disease, MRI classification, cogni- tive assessment, deep learning, multi-modal fusion.