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

Abstract

Recently, coronavirus disease (Covid-19) has become a serious public health threat, spreading worldwide in a very short time and threatening the lives of millions. With the increasing number of cases and mutations, medical resources are being drained day by day due to the rapid transmission of the disease, and the health systems of many countries are negatively affected. For this reason, it is very important to use available resources appropriately and timely for the detection and treatment of the disease. In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and to make the pre-diagnosis of Covid-19, and an alternative model (IsVoNet) was proposed. As a result of the training of the models, success accuracy of 99.92% in the VGG16 model, 99.65% in the ResNet50 model and 99.76% in the proposed model were obtained. According to the results, it was observed that the models classified Covid-19 and normal lung x-ray images with high accuracy and the proposed model showed a high success rate at lower time complexity than other models.