CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

TL;DR

CovidCTNet is an open-source deep learning model that significantly improves the accuracy of Covid-19 detection from CT images, outperforming radiologists and existing methods, and is designed for global deployment with heterogeneous data.

Contribution

The paper introduces CovidCTNet, a novel open-source deep learning framework that enhances CT-based Covid-19 detection accuracy to 90%, working effectively with diverse and small datasets.

Findings

CovidCTNet achieves 90% detection accuracy.

Outperforms radiologists' detection accuracy of 70%.

Designed for global use with heterogeneous data.

Abstract

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.