Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI Data

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses.