Automated detection of COVID-19 from chest X-rays using CNN

The novel coronavirus (COVID-2019), which first arrived in the Chinese city of Wuhan in December 2019, unrolled like a wind around the arena and brought into an epidemic and declared as a worldwide pandemic by WHO in march,2020. Agriculture, building, manufacturing, trade, lifestyle, tourism, and the global economy all suffer as a result of this disease. Consequently, it's very crucial to diagnose and treat the disease as soon as possible. According to radiology imaging methodologies radiological imaging techniques may aid in appropriately diagnosing and treating the condition with less response time. The utilization of raw chest X-ray pictures has been used to introduce a new model for the automatic detection of COVID-19 in this investigation. In this work we have built a binary classifier to detect covid-19 by deploying the deep learning technique–CNN on dataset collected from repository of JHU CSSE 2019 and was supported by JHU APL and transfer learning techniques has also been utilized to enhance the dataset. The best classification accuracy achieved by our model on chest X-ray dataset is 98.3%. We have also analyzed and compared the model in other research papers with our model.