@article{article_1085625, title={Application with deep learning models for COVID-19 diagnosis}, journal={Sakarya University Journal of Computer and Information Sciences}, volume={5}, pages={169–180}, year={2022}, DOI={10.35377/saucis...1085625}, url={https://izlik.org/JA25TL36UY}, author={Türk, Fuat and Kökver, Yunus}, keywords={COVID-19 diagnosis, DenseNet, NasNet-Mobile, deep learning classification}, abstract={COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.}, number={2}