COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches

• URL: http://arxiv.org/abs/2105.08506v2
• Date: Thu, 20 May 2021 08:47:45 GMT
• Title: COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches
• Authors: Sara Atito Ali Ahmed and Mehmet Can Yavuz and Mehmet Umut Sen and Fatih Gulsen and Onur Tutar and Bora Korkmazer and Cesur Samanci and Sabri Sirolu and Rauf Hamid and Ali Ergun Eryurekli and Toghrul Mammadov and Berrin Yanikoglu
• Abstract summary: We present a deep learning ensemble for detecting COVID-19 infection, combining slice-based (2D) and volume-based (3D) approaches. The proposed ensemble, called IST-CovNet, obtains 90.80% accuracy and 0.95 AUC score overall on the IST-C dataset. The system is deployed at Istanbul University Cerrahpasa School of Medicine.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the definitive RT-PCR test. We present a deep learning ensemble for detecting COVID-19 infection, combining slice-based (2D) and volume-based (3D) approaches. The 2D system detects the infection on each CT slice independently, combining them to obtain the patient-level decision via different methods (averaging and long-short term memory networks). The 3D system takes the whole CT volume to arrive to the patient-level decision in one step. A new high resolution chest CT scan dataset, called the IST-C dataset, is also collected in this work. The proposed ensemble, called IST-CovNet, obtains 90.80% accuracy and 0.95 AUC score overall on the 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 MosMed dataset that consists of COVID-19 scans and normal controls only. The system is deployed at Istanbul University Cerrahpasa School of Medicine.

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