Feature Extraction of Coronavirus X-Ray Images by RNN, Correlational Networks, and PNN

ABSTRACT

Today, the entire world is suffering and fear with the epidemic of coronavirus. The statistics of coronavirus as on current data, 213 countries affected by this pandemic, more than 1.1 cr people, suffering from this killer virus, and about 6L fatality are recording. This virus is spreading speedily, and the patients are mainly suffering from breathing. The patient having previous health issues will get more possibility of this disease. In this work, try to evaluate the COVID 19 patient x-ray images by using DL (deep learning) techniques developed on the grouping of a recurrent neural network (RNN) and a correlational network to identify COVID-19 automatically is used in the prediction of high-risk outbreaks to learn on the prognostic code sequences of patients. By use of RNNs helps the model to assess difference in patient status in terms of time and thereby improve predictive precision. A correlational neural network is to identify the salient features for CORONAVIRUS, and these features are feed into a Probabilistic neural network (PNN) for better corona diagnosis. The experimental result gives improved accuracy for analyzing coronavirus disease. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.