A Multilevel Deep Learning Model for earlier diagnosis of Covid-19 using CNN-LSTM

Abstract

These days, automatic recognition of disease has turned into a significant problem in clinical science because of fast populace development. An automatic infection recognition system helps specialists in the analysis of illness and gives accurate, reliable, and quick outcomes and diminishes the passing rate.Coronavirus (COVID-19) is one of such severe disease which spreads more fasteraround the world in recent times. Therefore, an automated disease recognition system, as the quickest analytic choice, ought to be executed to block the spreading of COVID-19. Through the medium of this paper, a deep learning model, both hybrid and novel in nature which uses a parallel union of a long short-term memory (LSTM) and convolutional neural network (CNN) is suggested for automatically detecting COVID-19from scanned images of chest X-ray. LSTM alongwith CNNcan extract the features in parallel, which can possibly gain more robust aspects that can be used for diagnosing Covid-19. Images of both the covid-19 patients and normal patients are included in the data set of the model. The experimental results show how accurately the proposed technique identifies the Covid-19 images from the normal based on the available data set. Our proposed architecture can help to detect the COVID-19 patients at ease without waiting for blood sample results to start with the treatment