Systematic Review and Metadata-Analysis of Covid-19 Detection and Classification Using Convolutional Neural Network and Chest x-ray images- recent advances

Fidelis Nfwan Gonten, Abdulsalam Ya'u Gital, Abuzairu Ahmad

Abstract


Several works were published in regards to the and early detection of COVID-19 via mathematical modelling and Convolutional neural network. The aim of this work is to provide the research community with systematic overview of the methods used in these studies as well as a collection of available open-source datasets in regards to COVID-19. In all, 51 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modelling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while the implementations were done via Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modelling and CNN have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected with their respective sources. However, much work is needed to be done in the diversification of the datasets. Other CNN and modelling applications in healthcare should be explored in regards to this COVID-19.


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