CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Javaheri, Tahereh; Homayounfar, Morteza; Amoozgar, Zohreh; Reiazi, Reza; Homayounieh, Fatemeh; Abbas, Engy; Laali, Azadeh; Radmard, Amir Reza; Gharib, Mohammad Hadi; Mousavi, Seyed Ali Javad; Ghaemi, Omid; Babaei, Rosa; Mobin, Hadi Karimi; Hosseinzadeh, Mehdi; Jahanban-Esfahlan, Rana; Seidi, Khaled; Kalra, Mannudeep K.; Zhang, Guanglan; Chitkushev, L. T.; Haibe-Kains, Benjamin; Malekzadeh, Reza; Rawassizadeh, Reza

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 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.