论文标题
深度学习用于筛选Covid-19使用胸部X射线图像
Deep Learning for Screening COVID-19 using Chest X-Ray Images
论文作者
论文摘要
随着对数百万个前瞻性“冠状病毒”或COVID-19病例的筛查的需求不断增长，并且由于在常用的PCR测试中出现了高假阴性，因此需要使用放射性图像（例如胸部X射线）进行替代的Covid-19的简单筛查机制，因此需要进行替代的简单筛选机制。在这种情况下，机器学习（ML）和深度学习（DL）提供了快速，自动化，有效的策略，以检测异常并提取改变的肺实质的关键特征，这可能与Covid-19病毒的特定签名有关。但是，可用的COVID-19数据集不足以训练深层神经网络。因此，我们提出了一个称为域扩展传输学习（DETL）的新概念。我们在一个相关的大胸部X射线数据集上使用预训练的深卷积神经网络，该数据集对四个类\ textit {viz。} $ normal $，$ pneumonia $，$其他\ _disease $和$ covid-ncivid-n $进行分类。进行了5倍的交叉验证，以估计使用胸部X射线诊断Covid-19的可行性。最初的结果表明了有望，并可能在更大，更多样化的数据集上复制。总体准确性衡量为$ 90.13 \％\ pm 0.14 $。为了了解COVID-19检测透明度，我们采用了梯度类激活图（Grad-CAM）的概念来检测模型在分类过程中更加关注的区域。发现这与专家证实的临床发现密切相关。
With the ever increasing demand for screening millions of prospective "novel coronavirus" or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes \textit{viz.} $normal$, $pneumonia$, $other\_disease$, and $Covid-19$. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as $90.13\% \pm 0.14$. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.
