تقرير
CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
| العنوان: | CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image |
|---|---|
| المؤلفون: | 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 |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2005.03059 |
| رقم الأكسشن: | edsarx.2005.03059 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |