A Modern Paradigm for Diagnosing Novel Coronavirus Disease (COVID-19) Using Multilayer Customized CNN via X-ray Images

The novel disease that has already been declared a global pandemic that is COVID-19, initially had an epidemic in a major Chinese city called Wuhan, China. This novel virus has now infected more than two hundred countries across the world as it propagates through human activity. In comparison, novel coronavirus signs are very close to general seasonal influenza such as common cold, fever, cough and shortness in breathing. Infected patient monitoring is viewed as a crucial phase in the battle against COVID-19. Detection tools for Positive cases of COVID-19 do not offers distinctive results, so that it has increased the need to support diagnostic tools. Therefore, to prevent further dissemination of this disease, it is extremely important as early as possible to identify positive cases. However, there will be some approaches for identifying positive patients of COVID-19 that are usually conducted on the basis of respiratory samples and amongst them, X-Ray or radiology images are an essential treatment course. Latest data from the techniques of X-Ray imaging show that these samples contain significant SARS-CoV-2 viruses. information. In order to reliably diagnose this virus, the use of deep learning techniques that is DNN which is also offers advanced imaging instruments and techniques will prove to be useful, as can the issue of the absence of trained rural physicians. In this report, we presented a multilayer customized convolution neural network (MC-CNN) system analyzing chest X-Ray images of individuals suffering from covid-19 using an open-source database available in kaggle. In order to propose DNN approach provides 97.36% of classification accuracy, 97.65% of sensitivity, and 99.28% of precision. Therefore, we conclude that this proposed approach will allow health professionals to confirm their initial evaluation of patients with COVID-19. © 2021, Springer Nature Singapore Pte Ltd.