论文标题
一种新颖而可靠的深度学习基于网络的工具，可检测胸部CT扫描中的Covid-19感染
A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan
论文作者
论文摘要
电晕病毒已经在许多国家 /地区遍布世界各地，并且夺走了许多生命。此外，世界卫生组织（WHO）宣布Covid-19已达到全球流行阶段。使用胸部CT扫描的早期可靠的诊断可以在重要情况下为医学专家提供帮助。在这项工作中，我们引入了计算机辅助诊断（CAD）Web服务，以在线检测Covid-19。该实验中使用了最大的公共CT-SCAN数据库之一，其中包含746名参与者。检查了许多著名的深层神经网络体系结构，这些神经网络体系结构由Resnet，Inception和Mobilenet组成，以找到混合系统的最有效模型。选择了密度连接的卷积网络（Densenet）的组合，以减少图像尺寸和NU-SVM作为反拟合瓶颈的NU-SVM，以区分COVID-19和健康对照。提出的方法可实现90.80％的召回，精度为89.76％，精度为90.61％。该方法还产生了95.05％的AUC。最终，通过NGROK使用训练有素的模型提供烧瓶Web服务，以提供静止的Covid-19探测器，该检测器仅需39毫秒即可处理一个图像。源代码也可在https://github.com/kilj4eden/covid_web上找到。根据发现，可以推断使用所提出的技术作为诊断Covid-19的自动化工具是可行的。
The corona virus is already spread around the world in many countries, and it has taken many lives. Furthermore, the world health organization (WHO) has announced that COVID-19 has reached the global epidemic stage. Early and reliable diagnosis using chest CT-scan can assist medical specialists in vital circumstances. In this work, we introduce a computer aided diagnosis (CAD) web service to detect COVID- 19 online. One of the largest public chest CT-scan databases, containing 746 participants was used in this experiment. A number of well-known deep neural network architectures consisting of ResNet, Inception and MobileNet were inspected to find the most efficient model for the hybrid system. A combination of the Densely connected convolutional network (DenseNet) in order to reduce image dimensions and Nu-SVM as an anti-overfitting bottleneck was chosen to distinguish between COVID-19 and healthy controls. The proposed methodology achieved 90.80% recall, 89.76% precision and 90.61% accuracy. The method also yields an AUC of 95.05%. Ultimately a flask web service is made public through ngrok using the trained models to provide a RESTful COVID-19 detector, which takes only 39 milliseconds to process one image. The source code is also available at https://github.com/KiLJ4EdeN/COVID_WEB. Based on the findings, it can be inferred that it is feasible to use the proposed technique as an automated tool for diagnosis of COVID-19.
