Exploring Deep Learning And Machine Learning Approaches For Brain Hemorrhage Detection
Keywords:
Brain Hemorrhage Detection, Computed Tomography (CT), Machine Learning, Deep Learning, Medical Image Processing, Feature Extraction, Classification, Artificial Intelligence in Healthcare.Abstract
Brain hemorrhage is a serious neurological condition that can lead to severe disability or death if not diagnosed promptly. Early identification of intracranial bleeding significantly improves patient survival rates and treatment outcomes. In clinical practice, computed tomography (CT) imaging is widely used for diagnosing neurological abnormalities due to its speed and accuracy. However, manual examination of CT scans by radiologists can be time-consuming and prone to human error, particularly when large numbers of images must be analyzed.Recent advances in artificial intelligence have enabled the development of automated systems capable of detecting and classifying brain hemorrhages with improved efficiency and reliability. This study presents a comprehensive review of existing approaches for brain hemorrhage detection based on both traditional machine learning and modern deep learning techniques. The review focuses on the major stages involved in automated detection systems, including image preprocessing, feature extraction, and classification. Furthermore, the performance of different algorithms is analyzed and compared based on evaluation metrics reported in previous studies.In addition, this paper discusses commonly used benchmark datasets employed for training and evaluating hemorrhage detection models. The advantages, limitations, and challenges associated with current methodologies are also highlighted. Finally, potential research directions are suggested to enhance detection accuracy, reduce computational complexity, and improve clinical applicability of automated diagnostic systems.
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