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

Ali Ahmed, Sara Atito and Yavuz, Mehmet Can and Şen, Mehmet Umut and Gülşen, Fatih and Tutar, Onur and Korkmazer, Bora and Samancı, Cesur and Şirolu, Sabri and Hamid, Rauf and Eryürekli, Ali Ergun and Mammadov, Toghrul and Yanıkoğlu, Berrin (2022) Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images. Neurocomputing, 488 . pp. 457-469. ISSN 0925-2312 (Print) 1872-8286 (Online)

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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 Cerrahpaşa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.
Item Type: Article
Uncontrolled Keywords: Computed Tomography; COVID-19; Deep Learning; Detection; Ensemble
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Berrin Yanıkoğlu
Date Deposited: 23 Aug 2022 20:30
Last Modified: 23 Aug 2022 20:30

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