Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images

Ahmed S. A. A., Yavuz M. C., Sen M. U., Gulsen F., TUTAR O., KORKMAZER B., ...More

NEUROCOMPUTING, vol.488, pp.457-469, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 488
  • Publication Date: 2022
  • Doi Number: 10.1016/j.neucom.2022.02.018
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, zbMATH
  • Page Numbers: pp.457-469
  • Keywords: COVID-19, Computed Tomography, Detection, Deep Learning, Ensemble, DIAGNOSIS, NETWORK
  • Istanbul University Affiliated: No

Abstract

Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpas & DBLBOND;a School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.(C) 2022 Elsevier B.V. All rights reserved.