论文标题
使用微调的深神经网络对COVID-19的自动诊断，具有有限的后胸部X射线图像
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
论文作者
论文摘要
新型冠状病毒2019（Covid-19）是一种类似于肺炎的呼吸道综合征。 Covid-19的当前诊断程序遵循基于逆转录酶聚合酶链反应（RT-PCR）方法，但是在初始阶段鉴定病毒的敏感性较小。因此，需要一种更健壮和替代的诊断技术。最近，随着电晕阳性患者的公开数据集的释放，包括计算机断层扫描（CT）和胸部X射线（CXR）成像；科学家，研究人员和医疗保健专家通过使用深度学习方法来鉴定肺部感染，从而更快，自动诊断Covid-19，以实现更好的治疗和治疗。这些数据集的样本有限，涉及阳性的Covid-19案例，这引起了无偏学习的挑战。 Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as后CXR图像的Covid-19，肺炎和正常情况）。曲线（AUC）下的准确性，精度，召回，损失和面积用于评估模型的性能。考虑到实验结果，每个模型的性能取决于方案。但是，与其他架构相比，NASNETLARGE的得分更好，这与其他最近提出的方法相比。本文还添加了视觉说明，以说明CXR图像中Covid-19的模型分类和感知的基础。
The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.
