Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification

Feng Shi, Liming Xia, Fei Shan, Bin Song, Dijia Wu, Ying Wei, Huan Yuan, Huiting Jiang, Yichu He, Yaozong Gao, He Sui, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making.

Original languageEnglish
Article number065031
JournalPhysics in Medicine and Biology
Volume66
Issue number6
DOIs
Publication statusPublished - 2021 Mar 21

Keywords

  • COVID-19
  • decision tree
  • pneumonia
  • random forest
  • size-aware

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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