Brain Hemorrhage CT Image Detection and Classification using Deep Learning Methods

Head injuries represent a significant challenge in modern medicine due to their potential for severe long-term consequences such as brain damage, memory loss, and other complications. Prompt and accurate diagnosis is essential for effective treatment; however, many healthcare systems face inefficiencies, resulting in delayed care. This research attempts to develop a robust machine learning (ML) model capable of accurately predicting the presence and type of brain hemorrhage from a CT scan dataset. In addition to detecting the presence of intracranial hemorrhages, the model proposed in this study identifies specific types of hemorrhages: intraventricular, intraparenchymal, subarachnoid, epidural, and subdural. The study’s findings indicate that ML can effectively facilitate a two-step diagnostic process: an initial binary classification to detect the presence of an injury, followed by a multilabel classification to identify the hemorrhage type. Future improvements in model quality are anticipated as more detailed and expansive datasets become publicly accessible.