A Novel 3D Recurrent R-CNN for Medical Imaging Feature Detection: A Case Study for Coronary Calcium Detection

Volume 16, Issue 2 (November 2025), pp. 29–39

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@article{jocse-16-2-6,
  author={Vikas Sarvasya and Robert Gotwals and Liam Butler},
  title={A Novel 3D Recurrent R-CNN for Medical Imaging Feature Detection: A Case Study for Coronary Calcium Detection},
  journal={The Journal of Computational Science Education},
  year=2025,
  month=nov,
  volume=16,
  issue=2,
  pages={29--39},
  doi={https://doi.org/10.22369/issn.2153-4136/16/2/6}
}

This student research project presents a pioneering network that utilizes specialized algorithms and propagation techniques to accurately identify small, dynamic structures in non-gated chest CT scans. The model's ability to provide reliable calcium scores enhances the clinical utility of chest CT scans, offering a promising tool for improving the diagnosis of coronary artery disease and optimizing the management of cardiac disease risk.