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

Sindhura, C and Al Fahim, M and Yalavarthy, PK and Gorthi, S (2023) Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage. In: Medical Physics .

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Official URL: https://doi.org/10.1002/mp.16714


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. 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. Results: The 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. Conclusion: The 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. © 2023 American Association of Physicists in Medicine.

Item Type: Journal Article
Publication: Medical Physics
Publisher: John Wiley and Sons Ltd
Additional Information: The copyright for this article belongs to the John Wiley and Sons Ltd.
Keywords: Automation; Clinical research; Computerized tomography; Convolutional neural networks; Diagnosis; Image reconstruction; Large dataset; Learning systems; Recurrent neural networks, Automated approach; Computed tomography images; Computed tomography scan; Haemorrage; Hemorrhage detection; Image-based; Intracranial hemorrhages; Learning frameworks; RNN; Sinograms, Classification (of information)
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 23 Nov 2023 05:21
Last Modified: 23 Nov 2023 05:32
URI: https://eprints.iisc.ac.in/id/eprint/83220

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