论文标题
OSEGNET：使用胸部X射线图像进行Covid-19检测的操作分割网络
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
论文作者
论文摘要
冠状病毒疾病（Covid-19）已被胸部X射线（CXR）图像自动诊断出自动诊断。但是，大多数早期的研究都使用了稀缺的数据集使用过过度拟合风险的深度学习模型。此外，先前的研究揭示了一个事实，即深网对分类不可靠，因为它们的决策可能起源于CXR上的无关区域。因此，在这项研究中，我们提出了操作分割网络（OSEGNET），该网络通过分割Covid-19肺炎进行可靠的诊断来进行检测。为了解决培训中遇到的数据稀缺性，尤其是在评估中遇到的稀缺性，这项研究扩展了最大的COVID-19 CXR数据集：QATA-COV19，其中包含121,378 CXR，包括9258 Covid-19的样品，其中包括其相应的地面裂解口罩，与研究社区公开共享。因此，Osegnet在最先进的深层模型中以99.09％的精度达到了99.65％的检测性能，精度为99.65％。
Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.
