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

TL;DR

This paper introduces IST-CovNet, a deep learning ensemble combining 2D slice-based and 3D volume-based approaches for COVID-19 detection in CT images, achieving high accuracy and AUC scores on new and public datasets.

Contribution

The paper presents a novel ensemble method for COVID-19 detection in CT scans, integrating 2D and 3D deep learning approaches and introducing a new high-resolution CT dataset.

Findings

Achieved 90.80% accuracy and 0.95 AUC on IST-C dataset.

Achieved 93.69% accuracy and 0.99 AUC on MosMed dataset.

Deployed system at Istanbul University Cerrahpasa.

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.