Graph Neural Network based Alzheimer's Disease Classification using Structural Brain Network

Alzheimer's disease (AD) is a chronic, incurable disorder that worsens with time and requires early diagnosis in order to treat and manage AD patients. Diffusion MR imaging and structural brain networks provide a great amount of information about the brain that hasn't been thoroughly investigated before. Graph Neural Networks (GNN) are used to process and learn the graph data structure in deep learning. There hasn't been any research on using anatomical brain networks with GNN to identity AD so far. In this paper, an efficient GNN architecture is used to classify individuals into Cognitively Normal (CN) and AD subjects using anatomical brain networks as graphs. The input labelled structural brain graphs of CN and AD are used to categorize AD and CN individuals using this GNN architecture. The proposed method is tested using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) research, which includes 100 CN and 62 AD subjects. The proposed system effectively demonstrates brain graph properties and provides a reliable Alzheimer's disease detection classifier. The deep learning system achieves a prediction accuracy of 97 percent, indicating that the proposed classification model is more resilient and perfect than the earlier methods. Our approach changes the way biomarkers of AD are detected and could provide clinicians with more confidence in automated AD diagnostic systems.