Classification and Detection of Covid 19 Using Deep Learning Techniques in Chest Radiographs

COVID-19, an infectious disease which causes respiratory distress syndrome, has affected a large number of patients.The increasing number of COVID-19 patients around the world and the limited number of detection kits pose a challenge in determining the presence of the disease. Imaging modalities such as X-rays are commonly used because they are readily available and cost-effective. In the face of the recent COVID-19, Deep Learning has proved to be an excellent tool because of the abundance of online medical images in various medical modalities, such as X-Ray, CT Scan, and MRI. A large number of medical research projects have been proposed and launched beginning in early 2020 because of its overwhelming use. Various deep learning techniques were evaluated for their ability to predict COVID-19 in this study. In comparison to the current literature, the proposed transfer learning approach is more successful. It is possible to classify COVID, Viral, and Bacterial pneumonia or a healthy patient using ResNet 18 Architecture’s four-class classifiers. A sustainable computing environment for training, validation, and analysis of the data has been developed. The proposed method achieved a 97 percent classification accuracy, 96 percent precision, and 98 percent recall in the case of COVID-19 detection using X-ray images, which demonstrates the importance of Deep Learning requirement in medical image diagnosis in the current need for time.