A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep learning methods for identifying hemorrhagic stroke on medical imaging scans has garnered significant interest in recent years. These methods are specifically designed to assist radiologists in identifying and highlighting abnormalities in medical scans. In this study, we conducted a systematic review and comprehensive gap analysis of various deep learning methodologies for automated hemorrhagic stroke identification. Our experiments were conducted using various datasets, particularly a publicly available large dataset of non-contrast computed tomography scans of the brain, in a regulated setting. Through an evaluation strategy that encompasses unexplored domain aspects, we identified the key challenges and areas for improvement. This strategy involved the utilization of multisite datasets and the evaluation based on the complexity in the appearance of hemorrhage. The subsequent analysis of the evaluation results revealed gaps in the current state of the art, emphasizing the need to bridge these gaps to achieve higher accuracy and reliability. This study offers valuable insights into the current state of automated hemorrhage stroke identification, highlighting areas for future research and development. The findings have significant implications for improving patient outcomes and alleviating the burden on radiology professionals, leading to more sustainable healthcare practices. The study concludes with recommendations for researchers in this field to foster the future development of automated tools for diagnosing hemorrhagic stroke.