Deep Learning Models To Diagnose Brain Stroke

Abstract

Brain Hemorrhage has gotten to be an extreme issue in the world. Brain Hemorrhage is also called ‘Brain Stroke’ in our country. The amount of the stroke may be a particularly difficult process among all of them; the brain stroke is the toughest one. There are three types of strokes. Hemorrhagic Stroke, Ischemic Stroke and Transient Ischemic Attack (TIA).One of the discrete methods that emerged as a first-line, radiation-free method of diagnosing brain stroke is magnetic resonance imaging (MRI). Most of the people (87%) are affected by Ischemic stroke. To distinguish this hemorrhage, we got to do a lab test at therapeutic. But that's exceptionally costly for our country. So, we choose to do something for them. After research, we developed a project which can detect this type of hemorrhage using deep learning neural network based algorithm. When it comes to picture identification, deep learning has made significant progress. To do that we collected raw data (CT Scan Copy) from a number of hospital, we preprocessed it with a help of radiologist and finally trained our model to nail our goal which is detecting hemorrhage stroke. Right now, we have been found that VGG-16 achieves a high rate of accuracy with a minimal level of complexity. It’s about 93 up percentage accuracy in total.Keywords: Brain Stoke, MRI, VGG-16.