论文标题
使用深卷积神经网络从胸部X射线图像诊断Covid-19
Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images
论文作者
论文摘要
COVID-19-19S流行病已成为全球主要的安全和健康威胁。成像诊断是筛选Covid-19的最有效方法之一。该项目利用几个开源或公共数据集介绍了COVID-19 CXRS的开源数据集，名为Covid-19-CXR-Dataset，并介绍了深度卷积神经网络模型。该模型在740个测试图像上验证了87.3％的精度，89.67％的精度和84.46％的召回率，并在95％置信区间的情况下正确地将98个Covid-11 X射线图像分类为100 COVID-11 X射线图像，超过81％的预测概率。该项目可以作为其他研究人员的参考，以促进医学成像中深度学习应用的开发。
The COVID-19 epidemic has become a major safety and health threat worldwide. Imaging diagnosis is one of the most effective ways to screen COVID-19. This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model. The model validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall, and correctly classifies 98 out of 100 COVID-19 x-ray images in test set with more than 81% prediction probability under the condition of 95% confidence interval. This project may serve as a reference for other researchers aiming to advance the development of deep learning applications in medical imaging.
