![]() ![]() Although the specificity of RT–PCR is sufficiently high for COVID-19, its sensitivity is relatively low in detecting COVID-19. Real-time polymerase chain reaction (RT–PCR), loop-mediated isothermal amplification (LAMP), antigen testing and other methods can be used to detect COVID-19. The common clinical symptoms are mainly respiratory symptoms, and some patients may have gastrointestinal symptoms. The characteristics of COVID-19 are diverse and unpredictable. It was named COVID-19 by the World Health Organization (WHO) in February 2020 around March 2020, the World Health Organization announced that the disease has affected the whole world and is a global pandemic disease. The outbreak was declared a Public Health Emergency of International Concern on 30 January 2020. ![]() The pandemic of global concern caused by COVID-19 has also brought enormous challenges to governments and the healthcare industry. X-ray is one of the most common radiological examination methods for screening and diagnosing chest diseases, as well as the main means of classifying and screening pneumonia, tuberculosis and breast cancer, and is a painless and noninvasive examination method suitable for high populations with relatively low costs. Radiologists can use CXR features to determine the type of pneumonia and the underlying pathogenesis. Chest X-rays (CXRs) play an important role in patient care. Pneumonia-type illnesses are more contagious during the flu season. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists. The experimental results show that the proposed model has good results in this work. The average accuracy for three category classification can reach 97.3%. The average accuracy of our model in detecting binary classification can reach 98.0%. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. This examination is not a substitute for a full physical examination, consultation, diagnosis, or treatment by your primary health care provider.Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. immigration purposes, nor is the physician required to provide you with diagnosis or treatment even though other matters related to your health might be discovered. ![]() The panel physician is not required to examine you for any conditions except those the U.S. Its purpose is to screen for certain medical conditions relevant to U.S. Note: The medical examination is not a complete physical examination. In other countries, the panel physician will give the applicant his/her medical exam results in a sealed envelope and an x-ray which the applicant must bring to the interview. In some countries, the panel physician will send the results to the U.S. The physical examination will at least include examination of the eyes, ears, nose and throat, extremities, heart, lungs, abdomen, lymph nodes and skin. The medical examination will include a medical history review, physical examination, chest X-ray and blood tests for syphilis. The applicant must show his/her passport (or other photo identification) and appointment letter to the doctor during the medical examination. ![]()
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