COVID-19 (Coronavirus-2019) is a disastrous pandemic which has affected the whole world damaging the whole ecosystem specially health. Researchers around the world have been contributing to the advancement in these conditions around the world. Medical practitioners have found that chest X-Ray images can used to detect whether a person suffers from covid-19 or not due to anomalies in chest radiography images. Continuing this motivation we have taken around 700 chest X ray images from different resources and applied two different transfer learning techniques to build a model which can detect the existence of covid-19 in a person. It uses VGG16 and Resnet50 deep learning models which utilize transfer learning to train their parameters. We have trained our both models for 50 epochs. VGG16 gives 76% of accuracy on test data and Resnet50 gives 85% accuracy on test data. We have tried to engineer thresholds for probability of classification thus changing specificity and sensitivity and also evaluated our models on various metrics such as classification report, confusion matrix heatmap, roc-auc score. This is by no means to be used for any medical procedures but can help other researchers to take some useful insights from it and carry forward the learning in building something which is production ready for contribution in our fight against COVID-19. © 2021 IEEE.
Diagnosis of COVID-19 from chest X-ray images using Deep Learning Model