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首页> 外文会议>National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering >Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning
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Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning

机译：基于VGG16的深度学习，自动检测来自胸部CT图像的胸腔内血管（Covid-19）

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摘要

In recent months, coronavirus disease 2019 (COVID-19) has infected millions of people worldwide. In addition to the clinical tests like reverse transcription-polymerase chain reaction (RT-PCR), medical imaging techniques such as computed tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 classification is done by a radiology expert. In this paper, we present a deep learning-based Convolutional Neural Network (CNN) model we developed for the classification of COVID-19 positive patients from healthy subjects using chest CT. We used 10979 chest CT images of 131 patients with COVID-19 and 150 healthy subjects for training, validating, and testing of the proposed model. Evaluation of the results showed the precision of 92%, sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and accuracy of 90%. We have used the regions of infection segmented by a radiologist to increase the generalization and reliability of the results. The plotted heatmaps show that the developed model has focused only on the infected regions of the lungs by COVID-19 to make decisions.
机译：最近几个月，2019年冠状病毒疾病（Covid-19）感染了全世界数百万人。除了逆转录聚合酶链反应（RT-PCR）等临床试验之外，还可以用作检测和评估Covid-19感染的患者的快速技术，例如计算机断层扫描（RT-PCR），例如计算机断层扫描（CT）。传统上，基于CT的Covid-19分类由放射学专家完成。在本文中，我们介绍了一个深入的学习卷积神经网络（CNN）模型，我们开发了使用胸部CT的健康受试者的Covid-19阳性患者的分类。我们使用了10979例COVID-19和150名健康受试者的131名患者的CT图像，用于训练，验证和测试所提出的模型。结果评价显示精度为92％，灵敏度为90％，特异性为91％，F1分数为0.91，准确度为90％。我们使用放射科学家分段的感染区域增加了结果的泛化和可靠性。绘制的热手套表明，开发的模型仅集中在Covid-19上的受感染的区域，以做出决定。

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