提高COVID-19 CT 图像质量和诊断准确性的深学习框架 (A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy)

We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes and improve the COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool for medical professionals in diagnosing and monitoring COVID-19.

翻译：我们为COVID-19的快速和准确CT(DL-FACT)测试提出了一个基于深学习的计算框架。我们的基于CT的DL框架是为提高COVID-19(及其变异)的测试速度和准确性而开发的,通过基于DL的提高和分类CT图像的方法,提高COVID-19(及其变异)的测试速度和准确性。图像增强网络是从DDnet改编的,DenseNet和Deconvolution(DenseNet)和Deconvolution(Deconvolution)网络简称改编的。为了显示其速度和准确性,我们评估了多个COVID-19(CT)图像来源的DL-FACT。我们的结果表明,DL-FACT可以大大缩短翻转时间从几天到几分钟,并将COVID-19测试的准确性提高到91%。DL-FACT可以用作诊断和监测COVID-19的医学专业人员的软件工具。