Deep learning approaches for Covid 19 classification

Abstract

Coronavirus Disease 2019 (COVID-19) is an infectious disease that newlinestarted to proliferate in Wuhan China, in December 2019. COVID-19 has a newlinedeath rate that is 5% of that of the 1918 Spanish flu pandemic. This disease is newlinecaused by the strain of Severe Acute Respiratory Syndrome Coronavirus 2 newline(SARS-CoV-2). Due to this catastrophe, the national governments have newlineintroduced a lockdown that prevents the spread of COVID-19 among the newlinehuman race. Till December 2020, there is no vaccination allocated for newlinediagnosing COVID-19. To protect the human race, we need an accurate newlineidentification process that detects the COVID-19 at an initial stage. Recent newlinestudies state that Chest X-ray (CXR) imaging is highly reliable than Reverse newlineTranscription Polymerase Chain Reaction (RT-PCR) by providing salient newlineinformation about the coronavirus, CXR is the fastest technique for newlineclassifying and diagnosing the COVID-19 disease. This CXR diagnosing newlinesupports clinical experts to initiate the treatment at the initial stage. In this newlineresearch work, two novel deep learning-based covid-19 disease prediction newlinemethods are proposed. The first method predicts covid-19 diseases from the newlinegiven input CXR images using Deep Convolutional Generative Adversarial newlineNetworks (DCGANs) with Convolution Neural Network (CNN). The second newlinemethod achieves the covid-19 disease prediction using the given input CXR newlineimages using Deep Convolutional Generative Adversarial Networks newline(DCGANs) with Deep Convolutional Neural Network (DCNET). The newlineproposed methods are tested for covid-19 disease prediction using four newlinedistinct datasets (COVID-19 X-ray, COVID-chest X-ray, COVID-19 newlineRadiography, and Corona Hack-chest X-ray) and the performance is analyzed newlineby using the performance parameters like Accuracy, Precision and Recall rate. newline