Transfer Learning-Based Approach for Identification of COVID-19

Corona virus Disease (COVID-2019) spread fast throughout the world, has infected millions of persons, and caused many fatalities. Mobilization has begun throughout the world for this pandemic that is still in existence, with certain constraints and measures being taken to keep this illness from spreading. Furthermore, to manage the illness, affected persons should be found. However, because of the inefficient amount of RT-PCR testing, chest computed tomography (CT) is a common means of supporting COVID-19 diagnosis. The notion of transfer learning was used in this work to detect the covid-19 from the X-ray pictures of the human body chest. With a total accuracy of 92% of the entire model, our model gives the identification of the Covid-19, 96% accuracy. The EfficientNet model previously trained on the Image-Net dataset is used in this study. This research study has customized the changes to the pre-trained model to fit our study and also added a pair of dense and dropout layers before the output layer.