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+# [HemorrhageDetection](https://hemorrhage.tkrsh.com/)
+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](https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=tjpx5GIAAAAJ)
+
+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
+