Effect on Radiological Imaging of COVID-19 using Deep Learning Techniques

Over the last several years, deep learning technology has been used to evaluate X-ray images of COVID-19 patients in order to diagnose the illness. Medical imaging applications typically have few pictures that are suitable for transfer learning. In this investigation, we have trained a deep neural network using more than 100,000 X-ray pictures. To diagnose COVID-19, two models were integrated. Experiments showed that two-class and three-class deep learning models were robust, with 93% and 92% sensitivities, respectively. As a component of a deep learning pipeline, this created an ensemble classifier and segmentation module to help identify probable COVID-19 patients. The model can achieve 90% accuracy and 91% sensitivity, according to a thorough comparison. Elucidate our deep learning process in detail and compare our model to other models that share a similar construction process.