Deep Learning approach to detect COVID-19 from X-ray Images

COVID-19 is a highly contagious viral infection that has a major impact on worldwide health. It also had a tremendous influence on the world economy. If positive cases are discovered early, the pandemic disease's spread can be hindered. Fever, cough, dyspnea, breathing issues, and viral pneumonia are among the flu-like symptoms experienced by COVID-19 patients. These signs, however, are negligible by themselves. Many individuals are asymptomatic yet have a positive COVID-19 chest CT scan and pathogenic test. As a result, in addition to symptoms, positive pathogenic tests and positive chest CT/X-Rays are used to diagnose the condition. In medical picture categorization, Deep Learning (DL) techniques, notably Convolutional Neural Networks (CNN), have been found to be successful. This study presents CNN and ResNet50 models for COVID-19 prediction from chest X-ray images. The findings achieved in COVID-19 prediction using CNN and ResNet50 with training and testing accuracy of 99.5 percent and 94 percent, respectively, highlight the applicability of Deep Learning models in illness prediction.