ABSTRACT
Currently, chest X-rays, as one of the auxiliary diagnostic methods for COVID-19, play an important role in the detection of COVID-19. In this paper, we propose an automatic detection method of COVID-19 chest X-ray based on Convolution Neural Network. In this method, we first get an image of the chest by X-ray, and then we preprocess the chest X-rays, and then send the preprocessed X-ray images to the convolutional neural network for feature extraction and classification, and finally we were able to diagnose whether COVID-19 or not. We test the three types of models (Inception V3, ResNet and DenseNet) under different layers, and finally propose the automatic detection method of COVID-19 chest X-ray. After a large number of experiments, the highest accuracy rate of COVID-19 detection is 98.059%. Compared with other studies in the same period, our accuracy rate is higher, and it is very fast. This indicates that our proposed method can be used as an auxiliary detection for COVID-19.
ABSTRACT
Background: Since the advent of the coronavirus disease 2019 (COVID-19) pandemic, in most parts of the world, people are still at risk of the disease. We aimed to establish a set of disability weights (DWs) for COVID-19 symptoms, evaluate the disease burden of inpatients, analyze the characteristics, and influencing factors of the disease. Methods: The symptoms were identified by literature review and medical staff questionnaire. DWs of COVID-19 symptoms were determined by the person-trade-off approach proposed by the World Health Organization. The extracted medical records data of 2,702 randomly selected inpatients with COVID-19 at three temporary military hospitals in Wuhan, China, were analyzed and used to calculate the disability adjusted life years (DALY). Means DALY between gender and age groups were tested by analysis of variance. Multiple line regression models were used to determine the relationship between DALY and age, gender, body mass index, length of stay, symptom duration before admission, and native place. Results: For the DALY of each inpatient, severe expiratory dyspnea and mild cough and sore throat had the highest (0.399) and lowest (0.004) weights, respectively. The average synthetic DALY and daily DALY were 2.29±1.33 and 0.18±0.15 days, respectively. Fever and fatigue contributed the largest DALY at 31.36%; nausea and vomiting, and anxiety and depression contributed the least at 7.05%. There were significant differences between gender and age groups in both synthetic and daily DALY. Age, body mass index, length of stay, and symptom duration before admission were strongly related to both synthetic and daily DALY. Conclusions: COVID-19 and its symptoms could cause heavy disease burden. Although the disease burden was higher among females than in the males; however, their daily disease burdens were similar. Life value differs for different age groups; taking the changing life value with age into account; the disease burden in the younger population was higher than that in the older population. Besides, treatment at the hospitals relieved the disease burden efficiently, while delay in hospitalization could worsen it. Therefore, deployment of adequate medical resources for early hospitalization of patients with moderate or severe symptoms is needed by the public health authority.