Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage.

Purpose: To propose an automated approach for detecting and classifying Intracranial Hemorrhages (ICH) directly from sinograms using a deep learning framework. This method is proposed to overcome the limitations of the conventional diagnosis by eliminating the time-consuming reconstruction step and minimizing the potential noise and artifacts that can occur during the Computed Tomography (CT) reconstruction process.

Methods: This study proposes a two-stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework.