卷积神经网络在新冠肺炎胸片检测中的应用

Ting Patrick, Kasam Anish, Lan Kevin
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引用次数: 6

摘要

从2019年底到现在,在全球防范COVID-19传播的努力中,帮助抗击大流行的最关键因素之一是开发了各种筛查方法,以尽可能方便和准确地检测COVID-19的存在。其中一种方法是利用胸部x射线(cxr)检测感染COVID-19的患者同时出现的异常。虽然产生结果的速度比传统的RT-PCR快得多,但cxr往往不太准确。意识到这一点,在我们的研究中,我们研究了计算机视觉的应用,以便更好地从cxr中检测COVID-19。再加上健康患者、非COVID-19诱发性肺炎患者和COVID-19阳性患者的大量cxr图像数据库,卷积神经网络(cnn)被证明具有在几秒钟内轻松准确识别患者是否感染COVID-19的能力。借用和调整三个经过良好测试的cnn架构:VGG-16, ResNet50和MobileNetV2,我们进行了迁移学习并训练了我们自己的三个模型,然后比较和对比了它们在正确标记患有和没有COVID-19的患者方面的不同精度,准确性和效率。最后,我们所有的模型都能够准确地对至少94%的cxr进行分类,其中一些表现得比其他的要好;这些性能上的差异很大程度上是由于我们的每个模型从三个各自的cnn借鉴了不同的架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of convolutional neural networks in chest X-ray analyses for the detection of COVID-19
Throughout global efforts to defend against the spread of COVID-19 from late 2019 up until now, one of the most crucial factors that has helped combat the pandemic is the development of various screening methods to detect the presence of COVID-19 as conveniently and accurately as possible. One of such methods is the utilization of chest X-Rays (CXRs) to detect anomalies that are concurrent with a patient infected with COVID-19. While yielding results much faster than the traditional RT-PCR test, CXRs tend to be less accurate. Realizing this issue, in our research, we investigated the applications of computer vision in order to better detect COVID-19 from CXRs. Coupled with an extensive image database of CXRs of healthy patients, patients with non-COVID-19 induced pneumonia, and patients positive with COVID-19, convolutional neural networks (CNNs) prove to possess the ability to easily and accurately identify whether or not a patient is infected with COVID-19 in a matter of seconds. Borrowing and adjusting the architectures of three well-tested CNNs: VGG-16, ResNet50, and MobileNetV2, we performed transfer learning and trained three of our own models, then compared and contrasted their differing precisions, accuracies, and efficiencies in correctly labeling patients with and without COVID-19. In the end, all of our models were able to accurately categorize at least 94% of the CXRs, with some performing better than the others; these differences in performance were largely due to the contrasting architectures each of our models borrowed from the three respective CNNs.
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