Abstract:
Brain hemorrhage is a serious medical condition that can cause damage to brain cells and increase pressure inside the skull, leading to complications such as coma or death. Early detection of brain hemorrhage is essential for timely medical intervention and better patient outcomes. Recent advances in machine learning and computer vision have led to the development of automated methods for detecting brain hemorrhage using medical images. This thesis aims to explore the feasibility and effectiveness of using machine learning algorithms for detecting brain hemorrhage in medical images.
