论文标题
使用Detrac深卷积神经网络将COVID-19在胸部X射线图像中分类
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
论文作者
论文摘要
胸部X射线是第一个在Covid-19疾病诊断中起重要作用的成像技术。由于大规模注释的图像数据集的可用性很高，因此使用卷积神经网络（CNN）取得了巨大的成功，以进行图像识别和分类。但是，由于注释的医学图像的可用性有限，医学图像的分类仍然是医学诊断中最大的挑战。多亏了转移学习，这是一种有效的机制，可以通过将知识从通用对象识别任务转移到特定领域的任务来提供有希望的解决方案。在本文中，我们验证并适应了我们先前开发的CNN，称为分解，转移和组成（DETRAC），以分类Covid-19胸部X射线图像。 DETRAC可以通过使用类分解机制研究其类边界来处理图像数据集中的任何不规则性。实验结果表明，DETRAC在从世界各地的几家医院收集的全面图像数据集中检测COVID-19病例中的能力。高精度为95.12％（敏感性为97.91％，特异性为91.87％，精度为93.36％），在检测正常和严重急性呼吸综合征病例的CoVID-19 X射线图像中可实现。
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.
