Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning

TL;DR

A neural network approach to find and classify the condition based upon the CT scan, which implements a time distributed convolutional network and observed accuracy above 92% from such an architecture, provided enough data.

Abstract

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.

Figures (5)

• Figure 1: Model Architecture
• Figure 2: Model Benchmark
• Figure 3: Accuracy And Loss
• Figure 4: Federated Step
• Figure 5: Types Of Hemorrhage Classified