چاپ صفحه |  صفحه نخست سامانه |  چکیده مقاله |  نویسندگان |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| 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. |
| نویسنده | نفر چندم مقاله |
|---|---|
| رعنا جهانبان اسفهلان | پانزدهم |
| خالد صیدی | شانزدهم |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| (69) nature digital medicine.pdf | 1399/12/07 | 2467348 | دانلود |
| code akhlagh.pdf | 1400/05/08 | 690086 | دانلود |
| top 1%.JPG | 1400/05/08 | 75547 | دانلود |
