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.
