Detection of COVID-19 from X-Ray Images

Abstract

Corona Virus Disease 2019 (COVID-19) is a new disease that is based on the SARSCoV- 2 virus. The virus has caused a worldwide pandemic due to its high infection rate and severity of symptoms. Several methods for detecting the virus exist among which different medical imaging modalities, in particular X-Ray imaging. In this thesis, we propose a three-phase machine learning approach to detect, from X-Ray images, whether a person is infected with the virus or not. The approach relies on an ensemble of customized convolutional neural networks to extract essential features from input images. The extracted features undergo fusion and are then passed on to a classifier for final results. We validated the approach on a set of 3,886 X-Ray images of patients carrying the virus, patients suffering from viral pneumonia, and healthy persons. When benchmarked against several models published in the literature, our proposed model outperformed them all.