Early Alzheimer’s Detection Using Multispectral Brain Oxygenation Imaging

Abstract

This paper presents an AI-based system for early detection of Alzheimer’s disease using multispectral imaging and cognitive assessment. The proposed system analyzes brain oxygenation patterns along with cognitive test performance to identify early indicators of cognitive decline. Meaningful biomarkers such as spectral intensity, memory accuracy, and reaction time are extracted and processed using machine learning techniques to classify individuals into low-, moderate-, or high-risk categories. The study demonstrates that integrating multispectral biomarkers with cognitive assessment features improves early risk stratification. This approach provides a fast, non-invasive, and accessible solution for early Alzheimer’s screening, supporting timely diagnosis and intervention.