An Explainable Multi-Model Ensemble for Brain Hemorrhage Detection and Subtype Classification Using 34,366 NCCT Images

Background

Brain hemorrhage is a critical neurological emergency and it is essential to quickly and accurately identify the type of bleeding if treatment is to be properly guided and patient outcomes are to be improved. Even though non-contrast computed tomography (NCCT) is still the main imaging technique used for the initial evaluation, diagnostic difficulties continue due to overlapping radiological patterns and the limitations involved in manual interpretation. The purpose of this study was to create and validate an ensemble deep learning approach for the automated multi-class classification of brain hemorrhage subtypes based on NCCT images.