COVID-19 Detection and Prediction of Chest X-Ray Images Using CNNs

The severe threat posed by the pandemic of Severe Acute Respiratory Syndrome—Coronavirus (SARS-CoV2/COVID-19) to the health as well as economic living society across the world has led the countries been struggling with ways to stop the spread of the pandemic. The cases have continued to spread at an exponential rate across 218 different countries. Statistics indicate that number of people infected with the disease are 29.3 million in India alone with 175 million global cases. The death toll has also gone up to 363K in India and 3.78 million worldwide. In our study, techniques based on deep CNNs to detect people affected by COVID using real-world datasets are discussed. To identify patients infected with COVID-19 from healthy patients, we took chest X-ray image data. Many published papers indicate that they have acquired over 97% accuracy of COVID-19 detection using CT scans, but X-ray detection still lags by a significant amount of margin. Many reference findings suggested that such an examination is important in COVID-19 discovery as X-beams are effectively accessible rapidly that too at low expenses. Consequently can be examined at normal stretches. In this study, we have made a robotized framework for recognizing infected patients from non-infected patients dependent on these chest X-beam pictures division. Our methodology setup will peruse the structure of a chest X-ray image and extract hidden features to detect COVID-19 patients differentiating them from pneumonia patients whose state of the art is less accurate as of now. The need for manual pre-processing steps is also reduced.