Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Joaquim De Moura,Plácido L Vidal,Marcos Ortega,Lucía Ramos,Jorge Novo

doi:10.3390/proceedings2020054031

Joaquim De Moura, Plácido L Vidal + Show 3 more

Open Access

https://doi.org/10.3390/proceedings2020054031

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Abstract

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

Highlights

The coronavirus disease 2019 (COVID-19) disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented public health crisis
This highly infectious disease was first identified in the city of Wuhan in Hubei province, China in December 2019
Satisfactory results were obtained that demonstrated the suitability of the proposed methodology to facilitate early diagnosis and, enable support to the clinical decision-making process in this pandemic scenario

Summary

Introduction

The coronavirus disease 2019 (COVID-19) disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented public health crisis. This highly infectious disease was first identified in the city of Wuhan in Hubei province, China in December 2019.

Results

Conclusion