Exploring Deep Learning and Machine Learning Approaches for Brain Hemorrhage Detection

Abstract

Brain hemorrhage, also known as intracranial hemorrhage (ICH), is a severe medical condition characterized by bleeding within the brain, often resulting in significant morbidity and mortality. Early detection and accurate classification of brain hemorrhage are critical for effective clinical intervention and improved patient survival rates. Diagnostic imaging techniques, particularly Computed Tomography (CT) scans, play a pivotal role in identifying brain abnormalities. However, manual analysis of CT images is time-consuming and prone to errors, necessitating the use of automated systems for enhanced accuracy and efficiency. This paper presents an overview of advanced methods utilizing Deep Learning (DL) and Machine Learning (ML) for the detection and classification of brain hemorrhages. It explores key processes such as image preprocessing, feature extraction, and classification, highlighting the strengths and limitations of various algorithms. A comprehensive analysis of benchmark datasets used for model training and testing is included, along with a comparative evaluation of different techniques. This study underscores the potential of DL and ML models to improve diagnostic accuracy and reduce human dependency in detecting brain hemorrhages. Additionally, the research identifies challenges in current methodologies and provides insights into future research opportunities, emphasizing the need for robust, scalable, and clinically viable solutions. The integration of advanced AI techniques in brain hemorrhage detection holds the promise of revolutionizing diagnostic processes, enhancing clinical outcomes, and paving the way for further innovations in medical imaging.