An Assessment of Covid19 Using CNN Architecture

Hundreds of thousands of people have been affected by the COVID-19 epidemic all over the world. The high mortality rate has impacted more than 200 countries. The Corona virus is highly contagious, which means patients are consistently waiting for a diagnosis, resulting in time-consuming radiologists and poor patient care. Using deep learning techniques to recognize COVID-19 from chest X-ray images is important to expedite the process. Our project will use these techniques in order to expedite the process. A patient's chest X-ray will be taken into account, and the output of the model will be labeled as COVID-positive or COVID-negative. Because of its relative speed, greater accuracy is achieved and processing delays are avoided as compared to conventional methods. A novel strain of Coronavirus (COVID-19) was discovered in Wuhan, a city in China, and is rapidly spreading around the globe. There are currently approximately 215 countries with COVID-19 illnesses. WHO reports 11,274,600 cases worldwide. Despite the increasing number of COVID-19 patients in hospitals, there are few resources available to stop the epidemic. For this reason, it is imperative to accurately diagnose COVID-19. In order to prevent the spread of the disease, patients with the disease must be diagnosed immediately. This study suggests a deep learning-based approach to differentiate patients with COVID-19 from those with viral pneumonia, bacterial pneumonia, and those with healthy (normal) lung function.