深度卷积神经网络从后前胸部X射线诊断COVID-19和其他肺炎

本文探讨了在327例健康的患者（152例患者），诊断为COVID-19（125例）和其他类型的肺炎（48例）的后前胸X线片上经过培训和测试的不同深度卷积神经网络体系结构。尤其是，本文着眼于深层卷积神经网络VGG16和VGG19，InceptionResNetV2和InceptionV3以及Xception，所有这些都紧随其后的是平坦的多层感知器和最终30％的退出率。..

Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays

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.