Modified VGG-16 model for COVID-19 chest X-ray images: optimal binary severity assessment

A pandemic caused by a virus known as COVID-19 has swept across the globe. One potential weapon in the fight against COVID-19 could be early detection through the use of chest X-ray images. In this paper, I have used modified VGG-16 deep learning model for binary classification of COVID-19 chest X-ray images. There are 16 weight layers in the standard VGG-16 model. In the suggested modified VGG model, the total number of weight layers has been reduced from 16 to 9 (eight convolutional layers and one fully connected layer). According to the results, the modified VGG-16 model performs better than the other three models (CNN, KNN and VGG-16) in terms of quantitative measures of accuracy, sensitivity and specificity. The dataset used for the proposed work consists of 24,000 chest X-ray images of lung collected from online depository comprising of 12,000 for each class (healthy and pneumonia).