An Ensemble-based Approach to the Detection of COVID-19 Induced Pneumonia using X-Ray Imagery
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AThe rapid emergence and spread of COVID-19 resulted in a surge in demand for laboratory-based testing globally. Currently, the gold standard diagnostic approach is large-scale molecular testing of biological samples which detect the SARS-CoV-2 virus RNA. Infrastructure limitations and supply shortages are limiting testing capacity with a growing demand for COVID-19 diagnostics across the EU. X-ray imagery is essential in establishing the severity of a multitude of diseases and monitoring responses of patients in the hospital setting. X-ray imagery should not be used to screen for or as a first-line test to detect COVID-19. However, X-ray presents an ideal opportunity to integrate additional screening measures into a pre-existing workflow. This paper investigates the utilisation of machine learning in automating the detection of COVID-19 induced pneumonia from X-Ray imagery.The approach will assist radiologists in the monitoring and differentiation of pneumonia caused by COVID-19 from other viral causes. A classification for the presence, or absence, of pneumonia caused by COVID-19 and other viral causes is derived. The paper contributes an initial investigation into an ensemble-learning based approach using transfer learning models VGG16, Inception and ResNet. The results of this work indicate an improved performance using ensemble-based learning as compared to an individual transfer learning model.