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

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.

5 figures

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Cited by in corpus (7)

• A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
• Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
• FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
• Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
• Weakly-supervised Generative Adversarial Networks for medical image classification
• PhD dissertation to infer multiple networks from microbial data
• DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19