COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

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.

COVID-ResNet：一种用于从射线照片中筛选COVID19的深度学习框架

在过去的几个月中，新颖的COVID19大流行已遍及全球。由于其易于传播，因此准确，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的开发技术至关重要。.. 最近的研究表明，患有COVID19的患者的胸部X光片显示了放射线照相中的某些异常。但是，这些方法是封闭的，无法提供给研究团体以提高可重复性并获得更深刻的见解。这项工作的目标是建立开放源代码和开放访问数据集，并提出一个准确的卷积神经网络框架，以区分COVID19病例与其他肺炎病例。我们的工作利用最先进的培训技术，包括逐步调整大小，周期性学习率查找和判别学习率，以训练快速，准确的残差神经网络。使用这些技术，我们在开放获取的COVID-19数据集上显示了最新技术成果。这项工作提出了一个三步技术来微调预训练的ResNet-50架构，以提高模型性能并减少训练时间。我们称之为COVIDResNet。这是通过将输入图像的大小逐步调整为128x128x3、224x224x3和229x229x3像素并在每个阶段微调网络来实现的。这种方法以及自动学习率选择功能使我们在仅41个历元的COVIDx数据集上就可以达到96.23％（在所有类别上）的最新准确性。这项工作提出了一种计算有效且高度准确的模型，用于与正常人一起对三种不同感染类型进行多分类。该模型可以帮助早期筛查COVID19病例，并有助于减轻医疗保健系统的负担。我们称之为COVIDResNet。这是通过将输入图像的大小逐步调整为128x128x3、224x224x3和229x229x3像素并在每个阶段微调网络来实现的。这种方法以及自动学习率选择功能使我们在仅41个历元的情况下就可以在COVIDx数据集上达到96.23％的最新准确性（在所有类别上）。这项工作提出了一种计算有效且高度准确的模型，用于与正常人一起对三种不同感染类型进行多分类。该模型可以帮助早期筛查COVID19病例，并有助于减轻医疗保健系统的负担。我们称之为COVIDResNet。这是通过将输入图像的大小逐步调整为128x128x3、224x224x3和229x229x3像素并在每个阶段微调网络来实现的。这种方法以及自动学习率选择功能使我们在仅41个历元的COVIDx数据集上就可以达到96.23％（在所有类别上）的最新准确性。这项工作提出了一种计算有效且高度准确的模型，用于与正常人一起对三种不同感染类型进行多分类。该模型可以帮助早期筛查COVID19病例，并有助于减轻医疗保健系统的负担。这种方法以及自动学习率选择功能使我们在仅41个历元的情况下就可以在COVIDx数据集上达到96.23％的最新准确性（在所有类别上）。这项工作提出了一种计算有效且高度准确的模型，用于与正常人一起对三种不同感染类型进行多分类。该模型可以帮助早期筛查COVID19病例，并有助于减轻医疗保健系统的负担。这种方法以及自动学习率选择功能使我们在仅41个历元的COVIDx数据集上就可以达到96.23％（在所有类别上）的最新准确性。这项工作提出了一种计算有效且高度准确的模型，用于与正常人一起对三种不同感染类型进行多分类。该模型可以帮助早期筛查COVID19病例，并有助于减轻医疗保健系统的负担。 （阅读更多）