A Deep Dive into Machine Learning and Deep Learning Techniques for Brain Hemorrhage Detection

A potentially deadly medical condition, brain hemorrhage impacts millions of people every year. Now more than ever, image-guided radiography is the gold standard for diagnosing and treating brain hemorrhages, greatly increasing the survival rate for patients. The identification and diagnosis of neurological illnesses has often made use of a computed tomography image. The radiologist must invest more time and effort into manually identifying abnormalities in the brain area from the CT scan. To quickly and accurately identify and categorize brain haemorrhage, new research has developed a number of methods based on Deep learning and conventional Machine Learning. This summary offers a thorough evaluation of the surveys that have been carried out using ML and DL. The preprocessing, feature extraction, and classification phases of brain hemorrhage are the primary foci of this study, along with their results and limitations. This comprehensive study also describes the current benchmark datasets used to examine the detection procedure. The analysis includes a thorough comparison of the performances. Additionally, this article discusses some features of the aforementioned method and offers suggestions for potential directions for further study.