  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.
