Master’s Project: Deep Learning-Based Multiclass Classification of Brain Stroke Using CT and MRI Imaging

This project focuses on developing a deep learning model to classify different types of brain stroke using medical imaging such as CT and MRI scans. The goal is to assist doctors in accurately identifying stroke types (e.g., ischemic, hemorrhagic) at an early stage.

The system uses convolutional neural networks (CNNs) to analyze brain images, extract important features, and categorize them into multiple classes. The dataset is preprocessed (resizing, normalization, augmentation) to improve model performance.

The final model aims to achieve high accuracy and provide fast predictions, which can support clinical decision-making and improve patient outcomes.