ABSTRACT
Covid-19 is has become an epidemic, which is affecting millions of people around the world. The common symptoms of Covid-19 are cough and fever, which are very similar to the normal Flu. Covid-19 spreads fast and is devastating for people of all ages especially elderly and people having weak immune system. The standard technique used for Covid-19 detection is real-time polymerase chain reaction (RT-PCR) test. However, RT-PCR is unreliable for Covid-19 detection as it takes long time to detect the disease and it produces considerable number of false positive cases. Therefore, we need to propose an automated and reliable method for Covid-19 detection. Radiographic images are widely used for the detection of various pulmonary diseases such as lung cancer, asthma, pneumonia, etc. We also used chest x-rays for the diagnosis of Covid-19. In this paper, we employed two deep learning models such as SqueezeNet and MobileNetv2 and fine-tuned to check the classification performance. Moreover, we performed data augmentation technique to increase the amount of data and avoid the overfitting of model. We evaluated the performance of the proposed system on standard dataset Covid-19 Radiography dataset that is publicly available. More specifically, we achieved remarkable accuracy of 97%, precision of 95.19%, recall of 100%, specificity of 95%, area under the curve of 98.93%, and F1-score of 97.06% on MobileNetv2. Experimental results and comparative analysis with other existing methods demonstrate that our method is reliable than PT-PCR and other existing state-of-the-art methods for Covid-19 detection. © 2021 IEEE.
ABSTRACT
BACKGROUND: Barts Health National Health Service Trust (BHNHST) serves a diverse population of 2.5 million people in London, UK. We undertook a health services assessment of factors used to evaluate the risk of severe acute respiratory coronavirus 2 (SARS-CoV-2) infection.METHODS: Patients with confirmed polymerase chain reaction (PCR) test results admitted between 1 March and 1 August 2020 were included, alongwith clinician-diagnosed suspected cases. Prognostic factors from the 4C Mortality score and 4C Deterioration scores were extracted from electronic health records and logistic regression was used to quantify the strength of association with 28-day mortality and clinical deterioration using national death registry linkage.RESULTS: Of 2783 patients, 1621 had a confirmed diagnosis, of whom 61% were male and 54% were from Black and Minority Ethnic groups; 26% died within 28 days of admission. Mortality was strongly associated with older age. The 4C mortality score had good stratification of risk with a calibration slope of 1.14 (95% CI 1.01-1.27). It may have under-estimated mortality risk in those with a high respiratory rate or requiring oxygen.CONCLUSION: Patients in this diverse patient cohort had similar mortality associated with prognostic factors to the 4C score derivation sample, but survival might be poorer in those with respiratory failure.