Brain Stroke Prediction Using Deep Learining

Abstract

A cerebrovascular accident (CVA), commonly known as a brain stroke, is a serious medical illness that can result in long-term impairments and even death. Early stroke risk prediction can assist healthcare workers in identifying persons at higher risk and providing appropriate interventions to prevent stroke occurrences. Many predictive methodologies, including as projecting disease occurrence, disease outcome, and assisting clinicians in disease treatment, have been widely used in clinical decision-making. This approach of predicting analytical techniques for stroke was conducted out using a deep learning network on a brain disease dataset. The purpose of this model is to construct a deep learning application that uses a convolution neural network to identify brain strokes. Furthermore, three models for forecasting outcomes have been constructed. A CT scan (computed tomography) image dataset is used in this proposed study to predict and classify strokes.