论文标题
深度卷积神经网络诊断COVID-19和其他肺炎疾病的后胸部X射线
Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays
论文作者
论文摘要
该文章探讨了对327例健康患者（152例患者）的胸部X射线训练和测试的不同深度卷积神经网络体系结构，被诊断为Covid-19（125）（125）（125）和其他类型的肺炎（48）。特别是，本文着眼于深度卷积神经网络VGG16和VGG19，InceptionResnetV2和InceptionV3以及Xception，然后都是平坦的多层感知器和最后的30％下降。该论文发现，性能最佳的网络是VGG16，最终的30美元$％辍学培训了3堂课（Covid-19，没有发现，其他肺炎）。它的内部交叉验证准确性为$ 93.9（\ pm3.4）$％，共证灵敏度为$ 87.7（-1.9，+2）$％，而没有发现敏感性为96.8（\ pm0.8）$％。相应的外部交叉验证值为$ 84.1（\ pm13.5）$％，$ 87.7（-1.9,2）$％，$ 96.8（\ pm0.8）$％。模型优化器为ADAM，其学习率为1E-4，并且分类横向渗透损失。希望，一旦将这项研究在医院进行练习，医疗保健专业人员将能够长期到长期通过机器学习工具诊断可能的肺炎诊断，如果检测到，是否可以与CoVID-19感染相关，是否可以与新的可能的Covid-19 Foyers发现在可能的“停留”之后，直到可以找到“锁定”的次数，直到锁定为“锁定”。此外，在短期内，希望从业者可以将深度卷积神经网络的诊断与可能的RT-PCR测试结果进行比较，并且如果发生冲突，可以进行计算机断层扫描，因为它们在显示Covid-19-199肺炎方面更为准确。
The article explores different deep convolutional neural network architectures trained and tested on posteroanterior chest X-rays of 327 patients who are healthy (152 patients), diagnosed with COVID-19 (125), and other types of pneumonia (48). In particular, this paper looks at the deep convolutional neural networks VGG16 and VGG19, InceptionResNetV2 and InceptionV3, as well as Xception, all followed by a flat multi-layer perceptron and a final 30% drop-out. The paper has found that the best performing network is VGG16 with a final $30$% drop-out trained over 3 classes (COVID-19, No Finding, Other Pneumonia). It has an internal cross-validated accuracy of $93.9(\pm3.4)$%, a COVID-19 sensitivity of $87.7(-1.9,+2)$%, and a No Finding sensitivity of $96.8(\pm0.8)$%. The respective external cross-validated values are $84.1(\pm13.5)$%, $87.7(-1.9,2)$%, and $96.8(\pm0.8)$%. The model optimizer was Adam with a 1e-4 learning rate, and categorical cross-entropy loss. It is hoped that, once this research will be put to practice in hospitals, healthcare professionals will be able in the medium to long-term to diagnosing through machine learning tools possible pneumonia, and if detected, whether it is linked to a COVID-19 infection, allowing the detection of new possible COVID-19 foyers after the end of possible "stop-and-go" lockdowns as expected by until a vaccine is found and widespread. Furthermore, in the short-term, it is hoped practitioners can compare the diagnosis from the deep convolutional neural networks with possible RT-PCR testing results, and if clashing, a Computed Tomography could be performed as they are more accurate in showing COVID-19 pneumonia.
