Advancing Alzheimer's Disease Detection Harnessing Graph Convolutional Networks For Enhanced Early Identification

Abstract

There is an urgent need for improved methods of early diagnosis in order to combat the devastating effects of Alzheimer's disease (AD), a major player among neurodegenerative diseases. Understanding the critical need for early detection to get the best possible therapy results, this research explores the use of the Graph Convolutional Networks (GCN) algorithm to detect early-stage Alzheimer's disease. The work captures detailed interactions between various brain areas by integrating neuro imaging data to generate brain connectivity diagrams. Improving GCN's performance in graph analysis, testing its discriminatory strength, and gauging its resilience on different datasets are the main aims of this study. We must study fresh and advanced methodologies since traditional diagnostic methods frequently fail to uncover subtle early-stage AD features. An exciting new direction for improving accuracy and enabling prompt intervention is the suggested GCN-based model, which aims to decode these subtle signals suggestive of AD. The research is in line with the overarching goal of improving the sensitivity and efficacy of AD diagnostic tools. There is a pressing need for more sophisticated methodology as traditional methods can miss small but significant changes in brain connections that occur before obvious symptoms. This study uses GCN to improve AD early detection methods, which might change the way the illness is diagnosed and treated forever.