Covid-19 detection on x-ray images using a deep learning architecture

Abstract

Coronavirus disease (Covid-19) has recently emerged as a serious public health threat, spreading rapidly worldwide and threatening millions of lives. With an increasing number of cases and mutations, medical resources are being drained daily owing to the rapid transmission of the disease, and the health systems of many countries are negatively affected. Therefore, it is important to use the available resources appropriately and in a timely manner to detect and treat the disease. In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and perform a prediagnosis of Covid-19, and an alternative model (IsVoNet) was proposed. Following model training, success accuracies of 99.92%, 99.65%, and 99.76% were achieved in the VGG16 model, ResNet50 model, and proposed model, respectively. According to the results, the models classified the Covid-19 and normal lung x-ray images with high accuracy, and the proposed model showed a high success rate at a lower time complexity than the other models.