AI-Driven Early Diagnosis of Alzheimer’s Disease Using Neuroimaging and Cognitive Scores
P Ratna Tejaswi,
S Narendra,
Nagavarapu Venkata Ramana Tilak,
Puli Vamshi Goud
Early detection of Alzheimer’s disease (AD) enables timely intervention, better patient management, and
improved outcomes. This paper reviews recent methods for early AD detection, proposes a multimodal
machine-learning framework combining structural MRI, resting-state fMRI, cognitive scores and plasma
biomarkers, and evaluates the approach on a benchmark dataset. Results show that multimodal fusion
with a lightweight 3D-CNN + transformer attention module improves classification of healthy controls,
mild cognitive impairment (MCI) and AD versus single-modality baselines, with higher sensitivity to
early (MCI → AD) converters. The study highlights trade-offs between accuracy, interpretability, and
clinical feasibility and outlines directions for translation to clinical practice.
