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 .

Introduction The use of new technologies such as the Internet of Things (IoT) in the management of chronic diseases, especially in the COVID pandemics, could be a life‐saving appliance for public health practice. The purpose of the current study is to identify the applications and capability of IoT and digital health in the management of the COVID‐19 pandemic. Methods This systematic review was conducted by searching the online databases of PubMed, Scopus, and Web of Science using selected keywords to retrieve the relevant literature published until December 25th, 2021. The most relevant original English studies were included after initial screening based on the inclusion criteria. Results Overall, 18 studies were included. Most of the studies reported benefits and positive responses in the form of patients' and healthcare providers' satisfaction and trust in the online systems. Many services were provided to the patients, including but not limited to training the patients on their conditions; monitoring vital signs and required actions when vital signs were altered; ensuring treatment adherence; monitoring and consulting the patients regarding diet, physical activity, and lifestyle. Conclusion IoT is a new technology, which can help us improve health care services during the COVID‐19 pandemic. It has a network of various sensors, obtaining data from patients. We have found several applications for this technology. Future studies can be conducted for the capability of other technologies in the management of chronic diseases.

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