论文标题
COVID-19使用转移学习与卷积神经网络检测
COVID-19 Detection using Transfer Learning with Convolutional Neural Network
论文作者
论文摘要
2019年新型冠状病毒疾病（Covid-19）是一种致命的传染病，于2019年12月在中国武汉武汉（Wuhan）首次认可，并且一直处于流行状态。在这种情况下，在感染者中发现Covid-19变得越来越重要。如今，与感染人群的数量相比，测试套件的数量逐渐减少。在最近的流行条件下，通过分析胸部CT（计算机断层扫描）对肺部疾病的诊断已成为COVID-19患者诊断和预言的重要工具。在这项研究中，已经提出了一种从CT图像检测COVID-19感染的转移学习策略（CNN）。在拟议的模型中，已经设计了具有转移学习模型V3的多层卷积神经网络（CNN）。与CNN类似，它使用卷积和汇总来提取特征，但是该传输学习模型包含数据集成像网的权重。因此，它可以非常有效地检测功能，从而使其具有更好的准确性。
The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China, and has gone on an epidemic situation. Under these circumstances, it became more important to detect COVID-19 in infected people. Nowadays, the testing kits are gradually lessening in number compared to the number of infected population. Under recent prevailing conditions, the diagnosis of lung disease by analyzing chest CT (Computed Tomography) images has become an important tool for both diagnosis and prophecy of COVID-19 patients. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed. Similar to CNN, it uses convolution and pooling to extract features, but this transfer learning model contains weights of dataset Imagenet. Thus it can detect features very effectively which gives it an upper hand for achieving better accuracy.
