Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.

An online survey and review about the awareness, coping style, and exercise behavior during the "COVID-19 pandemic situation" by implementing the cloud-based medical treatment technology system in China among the public.

To examine basic COVID-19 knowledge, coping style and exercise behavior among the public including government-provided medical cloud system treatment app based on the internet during the outbreak. Besides, to provide references for developing targeted strategies and measures on prevention and control of COVID-19. We conducted an online survey from 11th to 15th March 2020 via WeChat App using a designed questionnaire. As well as aim to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the "COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)" based on the Internet of Things. Valid information was collected from 1893 responders (47.07% males and 52.93% females aged 18-80 years, with a mean age of 31.05 ± 9.86) in 20 provincial-level regions across China. From the responders, 92.90% and 34.81% were scaled pass and good and above scores for the knowledge about the novel coronavirus epidemic. 38.44% were scaled poor scores and only 5.40% were scaled good and above scores for appropriate behavior coping with the pandemic. Among the responders, 52.14% reported having active physical exercise in various places during the previous 1 week. For all the responders, appropriate behavior coping correlated positively with physical exercise (p < 0.05); the daily consumed time for getting the epidemic-related information correlated positively with the score for cognition on the epidemic's prevention measures (r = 0.111, p < 0.01) and on general knowledge about the epidemic (r = 0.087, p < 0.01). Targeted and multiple measures for guidance on the control of COVID-19 among the public should be promoted to improve the cognition on basic knowledge, behaviors and treatment.

Deep Convolutional Neural Networks for Chest Diseases Detection.

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.