3D Convolutional Neural Networks for Improved Detection of Intracranial bleeding in CT Imaging
Bargava Subramanian, Naveen Kumarasami, Praveen Shastry, Kalyan Sivasailam, Anandakumar D, Elakkiya R, Harsha KG, Rithanya V, Harini T, Afshin Hussain, Kishore Prasath Venkatesh
TL;DR
The paper tackles the critical problem of rapid, accurate intracranial bleeding detection in CT scans within emergency settings. It introduces a U-shaped 3D CNN with CLAHE-based preprocessing to detect, classify, and segment major bleed types in volumetric CT data. Key contributions include a 23-layer encoder–decoder architecture with skip connections, thresholding-based ROI refinement, comprehensive data augmentation, and strong per-bleed-type performance (examples include high precision and localization accuracy across EDH, SAH, and other hemorrhages). The approach holds promise for scalable, real-time clinical workflows, with future work focused on dataset diversification, real-time inference, and multimodal data integration to broaden applicability and robustness.
Abstract
Background: Intracranial bleeding (IB) is a life-threatening condition caused by traumatic brain injuries, including epidural, subdural, subarachnoid, and intraparenchymal hemorrhages. Rapid and accurate detection is crucial to prevent severe complications. Traditional imaging can be slow and prone to variability, especially in high-pressure scenarios. Artificial Intelligence (AI) provides a solution by quickly analyzing medical images, identifying subtle hemorrhages, and flagging urgent cases. By enhancing diagnostic speed and accuracy, AI improves workflows and patient care. This article explores AI's role in transforming IB detection in emergency settings. Methods: A U-shaped 3D Convolutional Neural Network (CNN) automates IB detection and classification in volumetric CT scans. Advanced preprocessing, including CLAHE and intensity normalization, enhances image quality. The architecture preserves spatial and contextual details for precise segmentation. A dataset of 2,912 annotated CT scans was used for training and evaluation. Results: The model achieved high performance across major bleed types, with precision, recall, and accuracy exceeding 90 percent in most cases 96 percent precision for epidural hemorrhages and 94 percent accuracy for subarachnoid hemorrhages. Its ability to classify and localize hemorrhages highlights its clinical reliability. Conclusion: This U-shaped 3D CNN offers a scalable solution for automating IB detection, reducing diagnostic delays, and improving emergency care outcomes. Future work will expand dataset diversity, optimize real-time processing, and integrate multimodal data for enhanced clinical applicability.