ABSTRACT
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.
Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Deep Learning , Radiologists , SARS-CoV-2 , Tomography, X-Ray Computed , COVID-19/epidemiology , COVID-19 Testing/statistics & numerical data , Databases, Factual , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Diagnostic Errors/statistics & numerical data , Expert Testimony/statistics & numerical data , Humans , Lung/diagnostic imaging , Mathematical Concepts , Neural Networks, Computer , Pandemics , Radiologists/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical dataABSTRACT
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.