论文标题
Covid-Resnet：从X光片筛选CoVID19的深度学习框架
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs
论文作者
论文摘要
在过去的几个月中，这部小说《 Covid19大流行》在世界各地蔓延。由于其易于传输，开发技术以准确，轻松地识别Covid19的存在，并将其与其他形式的流感和肺炎区分开来是至关重要的。最近的研究表明，患有COVID19的患者的胸部X斑描绘了放射线照相中的某些异常。但是，这些方法是封闭的源头，并且无法提供研究界的重新可必续性并获得更深入的见解。这项工作的目的是建立开源和开放访问数据集，并提出一个准确的卷积神经网络框架，以将CoVID19病例与其他肺炎病例区分开。我们的工作利用了最先进的培训技术，包括渐进式调整，周期性学习率查找和歧视性学习率，以快速，准确的残留神经网络培训。使用这些技术，我们在Open-Access Covid-19数据集上展示了最先进的结果。这项工作提出了三步技术，可以微调预训练的Resnet-50体系结构，以提高模型性能并减少训练时间。我们称其为covidresnet。这是通过将输入图像逐步重新大小重新大小的128x128x3、224x224x3和229x229x3像素进行重新大小来实现的，并在每个阶段对网络进行微调。这种方法以及自动学习率的选择使我们能够在Covidx数据集上实现最高的96.23％（在所有类上）的状态，只有41个时代。这项工作提出了一种计算高效且高度准确的模型，用于与正常个体一起对三种不同感染类型进行多类分类。该模型可以帮助早期筛选CoVID19病例，并有助于减轻医疗保健系统的负担。
In the last few months, the novel COVID19 pandemic has spread all over the world. Due to its easy transmission, developing techniques to accurately and easily identify the presence of COVID19 and distinguish it from other forms of flu and pneumonia is crucial. Recent research has shown that the chest Xrays of patients suffering from COVID19 depicts certain abnormalities in the radiography. However, those approaches are closed source and not made available to the research community for re-producibility and gaining deeper insight. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. Our work utilizes state of the art training techniques including progressive resizing, cyclical learning rate finding and discriminative learning rates to training fast and accurate residual neural networks. Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.
