COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient.

Authors

  • Philip Meyer
    IT-Infrastructure for Translational Medical Research, University of Augsburg.
  • Dominik Müller
    IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany.
  • Iñaki Soto-Rey
    IT-Infrastructure for Translational Medical Research, University of Augsburg.
  • Frank Kramer
    IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany.