Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.

胸部 X 光图像用于深度卷积神经网络，用于检测 COVID-19，这是 21 世纪人类面临的最大挑战。对噪声的鲁棒性和泛化的改进是设计这些网络的主要挑战。在本文中，我们介绍了一种数据增强策略，该策略使用噪声密度的类型和值的确定来提高用于 COVID-19 检测的深度 CNN 的稳健性和泛化性。首先，我们提出了一种从学习到增强的方法，该方法可以生成具有优化噪声密度的原始图像数据的新噪声变体。我们应用贝叶斯优化技术来控制和选择最佳噪声类型及其参数。其次，我们提出了一种基于去噪 X 射线图像的新型数据增强策略，它使用去噪像素和原始像素之间的距离来生成新数据。我们开发了一个自动编码器模型，使用被高斯噪声和脉冲噪声破坏的去噪图像创建新数据。包含 COVID-19 阳性、健康和非 COVID 肺炎病例的胸部 X 光图像数据库用于微调预训练网络（AlexNet、ShuffleNet、ResNet18 和 GoogleNet）。与最先进的学习增强策略相比，所提出的方法在灵敏度 (0.808)、特异性 (0.915) 和 F-Measure (0.737) 方面表现更好。所提出方法的源代码可在 https://github.com/mohamadmomeny/Learning-to-augment-strategy 获得。包含 COVID-19 阳性、健康和非 COVID 肺炎病例的胸部 X 光图像数据库用于微调预训练网络（AlexNet、ShuffleNet、ResNet18 和 GoogleNet）。与最先进的学习增强策略相比，所提出的方法在灵敏度 (0.808)、特异性 (0.915) 和 F-Measure (0.737) 方面表现更好。所提出方法的源代码可在 https://github.com/mohamadmomeny/Learning-to-augment-strategy 获得。包含 COVID-19 阳性、健康和非 COVID 肺炎病例的胸部 X 光图像数据库用于微调预训练网络（AlexNet、ShuffleNet、ResNet18 和 GoogleNet）。与最先进的学习增强策略相比，所提出的方法在灵敏度 (0.808)、特异性 (0.915) 和 F-Measure (0.737) 方面表现更好。所提出方法的源代码可在 https://github.com/mohamadmomeny/Learning-to-augment-strategy 获得。737). 所提出方法的源代码可在 https://github.com/mohamadmomeny/Learning-to-augment-strategy 获得。737). 所提出方法的源代码可在 https://github.com/mohamadmomeny/Learning-to-augment-strategy 获得。