The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices.

中文翻译：

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从断层扫描图像中分割 COVID-19 病例感染性肺组织的轻量级 CNN 模型的性能分析


冠状病毒病 19 (COVID-19) 已在世界各地蔓延，造成了生存健康危机。通过计算机断层扫描 (CT) 图像自动检测肺部的 COVID-19 感染，在解决检测缓慢的问题和增强传统诊断程序方面具有巨大的潜力。然而，由于感染类型差异很大以及健康组织和受感染组织之间的对比度较低，因此从 CT 扫描中分割出 COVID-19 是有问题的。在对 COVID-19 进行肺部 CT 扫描分割时，需要快速、准确的结果，此外，由于大流行，大多数研究社区选择了各种基于云的服务器（例如 Google Colab 等）来开发算法。使用深度网络可以实现高精度，但由于资源在许多人之间共享，预测时间会有所不同，因此需要比较不同的轻量级分割模型。为了解决这个问题，我们的目标是使用四个卷积神经网络 (CNN) 来分析 COVID-19 的分割。我们数据集中的图像经过预处理，去除了运动伪影。这四个网络分别是UNet、分割网络（Seg Net）、高分辨率网络（HR Net）和VGG UNet。在包含 3,000 多张图像的数据集上进行训练后，HR Net 被发现是性能最佳的网络，准确率达到 96.24%，Dice 得分为 0.9127。分析表明，在从 CT 切片中分割由 COVID-19 引起的感染组织时，轻量级 CNN 模型比其他神经网络模型表现更好。