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

Abstract

AbstractPurposeTo 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.MethodsThis study proposes a two‐stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework. The first stage of the framework is Intensity Transformed Sinogram Sythesizer, which synthesizes sinograms that are equivalent to the intensity‐transformed CT images. The second stage comprises of a cascaded Convolutional Neural Network‐Recurrent Neural Network (CNN‐RNN) model that detects and classifies hemorrhages from the synthesized sinograms. The CNN module extracts high‐level features from each input sinogram, while the RNN module provides spatial correlation of the neighborhood regions in the sinograms. The proposed method was evaluated on a publicly available RSNA dataset consisting of a large sample size of 8652 patients.ResultsThe results showed that the proposed method had a notable improvement as high as 27% in patient‐wise accuracies when compared to state‐of‐the‐art methods like ResNext‐101, Inception‐v3 and Vision Transformer. Furthermore, the sinogram‐based approach was found to be more robust to noise and offset errors in comparison to CT image‐based approaches. The proposed model was also subjected to a multi‐label classification analysis to determine the hemorrhage type from a given sinogram. The learning patterns of the proposed model were also examined for explainability using the activation maps.ConclusionThe proposed sinogram‐based approach can provide an accurate and efficient diagnosis of ICH without the need for the time‐consuming reconstruction step and can potentially overcome the limitations of CT image‐based approaches. The results show promising outcomes for the use of sinogram‐based approaches in detecting hemorrhages, and further research can explore the potential of this approach in clinical settings.