Intelligent Medical Diagnosis Model Based on Graph Neural Networks for Medical Images

Abstract

Recently, numerous estimation issues have been solved due to the developments in data-driven artificial neural networks (ANN) and graph neural networks (GNN). The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format. However, physiological recordings often exhibit irregular and unordered patterns, posing a significant challenge in conceptualising them as matrices. As a result, GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system. Additionally, our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain. Subsequently, demographic data are included, and a multi-task learning architecture is devised, integrating classification and regression tasks. The trials used an authentic dataset, including 800 brain x-ray pictures, consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases. Based on empirical evidence, our methodology demonstrates superior performance in classification, surpassing other comparison methods with a notable achievement of 92.27% in terms of area under the curve as well as a correlation coefficient of 0.62.