论文标题
在多级X射线胸部图像上检测具有卷积神经网络特征的Covid-19患者
Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images
论文作者
论文摘要
Covid-19是一种非常严重的致命疾病，已被世界卫生组织（WHO）宣布为大流行。全世界正在努力终止Covid-19-19的大流行，这使各国尽快陷入严重的健康和经济问题。其中最重要的是正确识别获得Covid-19的人。支持逆转录聚合酶链反应（RT-PCR）测试的方法和方法已经开始在文献中进行。在这项研究中，使用Covid-19攻击了呼吸系统，因此可以轻松，快速地使用胸部X射线图像。通过使用这些图像的卷积神经网络模型之一提取的功能，已通过使用剩余网络提取的功能（Resnet-50）获得了使用支持向量机的分类性能。虽然使用支持矢量机（SVM）获得了Covid-19的检测，其敏感性值为96.35％，使用5倍的交叉验证方法获得了最高灵敏度值，但SVM- Quadratic和SVM-Cibic均在99％以上的SVM- Quadratic和SVM-Cibic中都检测到了最高的总体性能值。根据这些较高的结果，人们认为该方法已经进行了研究，将有助于放射学专家并降低错误检测率。
Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid-19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.
