论文标题
面膜RCNN的重新连接骨架的比较性能分析，以分段胸部CT扫描中的covid-19符号
Comparative performance analysis of the ResNet backbones of Mask RCNN to segment the signs of COVID-19 in chest CT scans
论文作者
论文摘要
Covid-19对于全球的死亡人数和关键患者数量增加而有害。根据开发计划署（联合国家发展计划）的社会经济计划，针对Covid-19危机，大流行远不止是健康危机：它影响了其核心社会和经济。最近，基于胸部X射线的成像技术是COVID-19诊断的一部分，尤其是使用卷积神经网络（CNN）来识别和分类图像的一部分。但是，鉴于受监督标记的成像数据的局限性，医学诊断的分类和预测风险建模往往会妥协。本文旨在通过对肺CT（胸部计算机断层扫描）肺部进行深层神经网络来识别和监测COVID-19对人肺的影响。我们采用了带有RESNET50和RESNET101的Mask RCNN作为骨干，以分割受Covid-19冠状病毒影响的区域。使用症状表现出的人类肺部的区域，该模型将患者的状况分类为“轻度”或“令人震惊的”。此外，该模型已部署在Google Cloud平台（GCP）上，以模拟该模型的在线用法，以进行性能评估和准确性改进。 RESNET101骨干模型的F1得分为0.85，并且平均时间为9.04秒，而预测得分更快。
COVID-19 has been detrimental in terms of the number of fatalities and rising number of critical patients across the world. According to the UNDP (United National Development Programme) Socio-Economic programme, aimed at the COVID-19 crisis, the pandemic is far more than a health crisis: it is affecting societies and economies at their core. There has been greater developments recently in the chest X-ray-based imaging technique as part of the COVID-19 diagnosis especially using Convolution Neural Networks (CNN) for recognising and classifying images. However, given the limitation of supervised labelled imaging data, the classification and predictive risk modelling of medical diagnosis tend to compromise. This paper aims to identify and monitor the effects of COVID-19 on the human lungs by employing Deep Neural Networks on axial CT (Chest Computed Tomography) scan of lungs. We have adopted Mask RCNN, with ResNet50 and ResNet101 as its backbone, to segment the regions, affected by COVID-19 coronavirus. Using the regions of human lungs, where symptoms have manifested, the model classifies condition of the patient as either "Mild" or "Alarming". Moreover, the model is deployed on the Google Cloud Platform (GCP) to simulate the online usage of the model for performance evaluation and accuracy improvement. The ResNet101 backbone model produces an F1 score of 0.85 and faster prediction scores with an average time of 9.04 seconds per inference.
