Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach

BackgroundIntracranial hemorrhage (ICH) is a life-threatening medical emergency requiring rapid and accurate diagnosis. Non-contrast computed tomography (CT) remains the primary imaging modality for detecting acute hemorrhage. In recent years, machine learning (ML) and deep learning (DL) approaches have gained increasing attention for automated detection and classification of ICH and its subtypes. This systematic review aims to consolidate and critically analyze contemporary machine learning and deep learning methodologies applied to ICH detection and classification from non-contrast CT scans.MethodsA comprehensive review of published studies was conducted focusing on ML and DL models developed for identifying ICH and its subtypes, including epidural, subdural, intraparenchymal, intraventricular, and subarachnoid hemorrhages. The reviewed techniques encompass conventional convolutional neural networks (CNNs), three-dimensional CNNs, hybrid and ensemble frameworks, and emerging transformer-based architectures. Preprocessing strategies such as Hounsfield Unit windowing, skull stripping, and data augmentation were examined. Additionally, explainable artificial intelligence (XAI) approaches, including Grad-CAM, were evaluated for enhancing model interpretability.ResultsRecent studies demonstrate promising diagnostic performance across multiple deep learning architectures, with improved sensitivity and specificity for subtype classification. Hybrid and transformer-based models show enhanced feature representation capabilities. Preprocessing techniques and explainability methods contribute significantly to model robustness and clinical interpretability.ConclusionMachine learning and deep learning models exhibit substantial potential in automated ICH detection and classification from non-contrast CT scans. However, challenges remain regarding generalizability, dataset heterogeneity, and clinical validation. Future research should emphasize large-scale multi-center validation, model interpretability, and integration into real-world clinical workflows to enable effective translation into routine neuroimaging practice.