论文标题
使用卷积神经网络对CT扫描的COVID-19自动检测
Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
论文作者
论文摘要
Covid-19是一种传染病，会导致与SARS-COV引起的呼吸道问题（2003年）。当前，拭子样品正在用于诊断。最常见的测试方法是RT-PCR方法，该方法具有高特异性但敏感性可变。基于AI的检测有能力克服此缺点。在本文中，我们提出了一种前瞻性方法，其中我们使用胸部CT扫描来诊断患者的肺炎。我们使用一组开源图像，可作为单个CT切片，以及来自私人印度医院的完整CT扫描来培训我们的模型。我们使用U-NET体系结构构建一个2D分割模型，该模型通过标记感染区域来提供输出。我们的模型的灵敏度为96.428％（95％CI：88％-100％），特异性为88.39％（95％CI：82％-94％）。此外，我们得出了将切片级预测转换为扫描级别的逻辑，这有助于我们减少误报。
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most common testing method used is the RT-PCR method, which has high specificity but variable sensitivity. AI-based detection has the capability to overcome this drawback. In this paper, we propose a prospective method wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 96.428% (95% CI: 88%-100%) and a specificity of 88.39% (95% CI: 82%-94%). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.
