An Intelligent Framework for Early Detection of Brain Haemorrhage Using Graph Neural Network

Brain hemorrhage is a serious medical illness that can have life-threatening effects. Fast and precise diagnosis is essential for successful treatment. In this work, we apply Graph Neural Networks (GNN) to CT scans to offer a novel method for the early diagnosis of brain bleeding. Using our approach, CT scan pictures are transformed into graph representations, where each voxel or region of interest is handled as a network node. Graph Convolutional Networks (GCN) are utilized to extract features from these graph representations and categorize them into situations that are hemorrhagic or non-hemorrhagic. To extract pertinent characteristics and create adjacency matrices that show the spatial relationships between voxels, the process starts with preprocessing CT scans. The building of graphs is based on these adjacency matrices. After that, we use the generated graphs to train a GCN model that can recognize patterns suggestive of bleeding and capture intricate spatial dependencies. This approach can identify early-stage hemorrhage by identifying tiny irregularities through learning from the graph structure. This method was tested on a dataset of CT images from patients with known hemorrhagic disorders, and the results showed promise in identifying brain bleeding early on using GNNs with accuracy of 86.19%. Our suggested approach provides radiologists with an automated, non-invasive way to help them quickly diagnose brain bleeding, which will enable quicker interventions and better patient outcomes. This discovery has great promise for improving the provision of neurology healthcare while also advancing medical image analysis tools.