About Dataset

The dataset used in this project contains two classes of images, with one class representing individuals who have been diagnosed with acute stroke and the other class representing individuals who have not been diagnosed with acute stroke. The dataset consists of a total of 5029 images. Data augmentation techniques were applied to the images to improve the overall accuracy of the model. The augmentation techniques include image flipping, rotation, and scaling. This helped in creating a diverse and robust dataset that can better represent the real-world scenario. The dataset is an important resource for researchers and healthcare professionals working in the field of stroke medicine, as it provides a large and diverse set of images that can be used to train machine learning models to detect and diagnose stroke in patients