Performance Result for Detection of COVID-19 using Deep Learning

Abstract

The 2019 novel coronavirus (COVID-19), which has sprawled fleetly among masses residing in distant nations, had a prefatory juncture in China. From both a safeness and a lucrative outlook, it has staggered the world with its hasty diffusion with conjectural vicious generic repercussions for the masses. Consequent to the escalating cases daily, there is a constricted fraction of COVID-19 inspection kits acquirable in healthcare institutions. Ergo, to obviate COVID-19 propagating betwixt masses, it is imperative to enforce an instinctive unveiling network as a prompt jack legging diagnosis appendage. The contemplated method embroils a convolutional neural network- based model, namely ResNet50, concerted with a Fully Connected Layer (FCL), reinforced by Rectified Linear Unit (ReLU) for the unearthing of coronavirus pneumonia imparted sufferer by harnessing chest X-ray radiographs. The endorsed classification model, i.e. resnet50 affirmed by FCL and ReLU, compassed accuracy of 94% for unearthing COVID-19. When equated to diverse classification models, the purported model is preeminent. The aftereffect is premised on the attested X-ray images from the data appropriable in the arsenal of Kaggle.