CovidCTNet: an open-source deep learning approach to diagnose Covid-19 using small cohort of CT images
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Citation (published version)T. Javaheri, M. Homayounfar, Z. Amoozgar, R. Reiazi, F. Homayounieh, E. Abbas, A. Laali, A.R. Radmard, M.H. Gharib, S.A.J. Mousavi, O. Ghaemi, R. Babaei, H.K. Mobin, M. Hosseinzadeh, R. Jahanban-Esfahlan, K. Seidi, M.K. Kalra, G. Zhang, L. Chitkushev, B. Haibe-Kains, R. Malekzadeh, R. Rawassizadeh. 2021. "CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images." npj Digital Medicine, https://doi.org/10.1038/s41746-021-00399-3
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 transcriptasepolymerase 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 framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
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