HemorrhageDetection

Our model can detect acute intracranial haemorrhage and its sub types with inference times less than 1 second and accuracy greater than 85%. We aim to provide automated, faster and more accurate diagnosis using computer vision and deep residual learning frameworks for image recognition and classification

Research Paper

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 a Computed tomography (CT) scan of the patient and identifying the
location and type of hemorrhage if one exists. When a patient shows acute neurological symptoms such
as severe headache or loss of consciousness, highly trained specialists review medical images of the
patient’s cranium to look for the presence, location and type of hemorrhage. The process is complicated
and often time consuming as these scans are essentially a combination of multiple X-Ray scans
processed by a computer. To reduce delays which lead to deaths we propose a solution to this problem
based on a Machine Vision based approach to automate the detection of intracranial hemorrhaging. We
employ a modified version of ResNet 34 trained on a dataset provided by Radiological Society of North
America. These convolutional networks are then stacked using a sequence model (such as a recurrent
neural network) to preserve temporal information. The system can detect acute intracranial hemorrhage
and its subtypes with inference times less than 1 second and accuracy greater than 85%. We aim to
provide automated, faster and more accurate diagnosis using computer vision and deep residual learning
frameworks for image recognition and classification

Demo_Models : https://drive.google.com/drive/folders/1YL_mnlDORfGtyGGP7iEm1C7hwzPM-cek?usp=sharing