  Intracranial hemorrhage, bleeding that occurs inside the cranium, is a
serious health problem requiring rapid and often intensive medical treatment.
Such a condition is traditionally diagnosed by highly-trained specialists
analyzing computed tomography (CT) scan of the patient and identifying the
location and type of hemorrhage if one exists. We propose a neural network
approach to find and classify the condition based upon the CT scan. The model
architecture implements a time distributed convolutional network. We observed
accuracy above 92% from such an architecture, provided enough data. We propose
further extensions to our approach involving the deployment of federated
learning. This would be helpful in pooling learned parameters without violating
the inherent privacy of the data involved.
