A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases.

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) continues to have a catastrophic impact on the living standard of people worldwide. To fight against COVID-19, many countries are using a combination of containment and mitigation activities. Effective screening of contaminated patients is a critical step in the battle against COVID-19. During the early medical examination, it was observed that patient having abnormalities in chest radiography images shows the symptoms of COVID-19 infection. Motivated by this, in this article, we proposed a unique framework to diagnose the COVID-19 infection. Here, we removed the fully connected layers of an already proven model VGG-16 and placed a new simplified fully connected layer set that is initialized with some random weights on top of this deep convolutional neural network, which has already learned discriminative features, namely, edges, colors, geometric changes,shapes, and objects. To avoid the risk of destroying the rich features, we warm up our FC head by seizing all layers in the body of our network and then unfreeze all the layers in the network body to be fine-tuned.The suggested classification model achieved an accuracy of 97.12% with 99.2% sensitivity and 99.6% specificity for COVID-19 identification. This classification model is superior to the other classification model used to classify COVID-19 infected patients.