Brain Stroke Prediction Using Visual Geometry Group Model

Stroke has become the leading cause of high mortality and disability rates in the modern era. Early detection and prediction of stroke can significantly improve patient outcomes. In this study, we propose a deep learning approach using the Visual Geometry Group (VGG-16) model. VGG-16 is a type of Convolutional Neural Network (CNN) which is one of the best computer vision models to date to predict the occurrence of a stroke in the brain. VGG-16 is a type of CNN that is one of the best computer vision models to date. We used a dataset consisting of Magnetic resonance imaging (MRI) images of patients with and without stroke. The VGG-16 model was pre-trained on the ImageNet dataset and fine-tuned on our dataset to predict the occurrence of a stroke. Our experimental results demonstrated that the proposed approach achieves high accuracy and can effectively predict stroke occurrence. We have also conducted an extensive analysis of the model's performance and provided insights into important features used by the model to predict stroke occurrence. The proposed approach has the potential to be used in clinical settings to aid in the early detection and prevention of stroke.