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Detecting COVID-19 in computed tomography (CT) or radiography images has beenproposed as a supplement to the definitive RT-PCR test. We present a deeplearning ensemble for detecting COVID-19 infection, combining slice-based (2D)and volume-based (3D) approaches. The 2D system detects the infection on eachCT slice independently, combining them to obtain the patient-level decision viadifferent methods (averaging and long-short term memory networks). The 3Dsystem takes the whole CT volume to arrive to the patient-level decision in onestep. A new high resolution chest CT scan dataset, called the IST-C dataset, isalso collected in this work. The proposed ensemble, called IST-CovNet, obtains90.80% accuracy and 0.95 AUC score overall on the IST-C dataset in detectingCOVID-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 thatconsists of COVID-19 scans and normal controls only. The system is deployed atIstanbul University Cerrahpasa School of Medicine.

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