  Haemorrhaging of the brain is the leading cause of death in people between
the ages of 15 and 24 and the third leading cause of death in people older than
that. Computed tomography (CT) is an imaging modality used to diagnose
neurological emergencies, including stroke and traumatic brain injury. Recent
advances in Deep Learning and Image Processing have utilised different
modalities like CT scans to help automate the detection and segmentation of
brain haemorrhage occurrences. In this paper, we propose a novel implementation
of an architecture consisting of traditional Convolutional Neural Networks(CNN)
along with Graph Neural Networks(GNN) to produce a holistic model for the task
of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood
aggregation thus providing a reliable estimate of global structures present in
images. GNNs work with few layers thus in turn requiring fewer parameters to
work with. We were able to achieve a dice coefficient score of around 0.81 with
limited data with our implementation.
