Automatic Segmentation of COVID-19 Infection on Lung CT Scans using Mask R-CNN

DANDIL E., YILDIRIM M. S.

4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022, Ankara, Türkiye, 9 - 11 Haziran 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/hora55278.2022.9800029
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: automatic segmentation, Coronavirus, COVID-19, deep learning, disease diagnosis, Mask R-CNN
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.The novel of coronavirus disease (COVID-19) emerged as an infection threatening all humanity and later became a pandemic. The most common known symptoms of COVID-19 infection are dry cough, sore throat/inflammation and fever. The disease continues as a form of extreme pneumonia in the lung in the later stages and may cause permanent damage to the lung. Therefore, automated computer-assisted methods can assist in diagnosing COVID-19 infection at an early stage. In this study, we propose a robust method based on Mask R-CNN for automatic segmentation of COVID-19 infections and lung abnormalities on a publicly-available dataset. Experimental studies for segmentation of COVID-19 infections using CT scans achieved Dice similarity score (DSC) of 81.93% on the dataset. As a result, in this study, it is revealed that Mask R-CNN method for segmentation of COVID-19 infections is successful and can help physicians in decision-making.