ÇİÇEKLİ, NAHİDE ZEYNEPBAYDOĞMUŞ, GÖZDE KARATAŞ2023-04-042023-04-042022N. Z. Cicekli and G. K. Baydogmus, "Covid-19 Disease Detection with Improved Deep Learning Algorithms on X-Ray Data," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 2022, pp. 1-4, doi: 10.1109/HORA55278.2022.9800070.978-166546835-0https://doi.org/10.1109/HORA55278.2022.9800070https://hdl.handle.net/11413/8425The COVID-19 pandemic has brought human life to a startling halt around the world from the moment it emerged and took thousands of lives. The health system has come to the point of collapse, many people in the world have died from being infected, and many people who have survived the disease have had permanent lung damage with the spread of COVID-19 in 212 countries and regions. In this study, an answer is sought to diagnose the disease-causing virus through Artificial Intelligence Algorithms. The aim of the study is to accelerate the diagnosis and treatment process of COVID-19 disease. Enhancements were made using Deep Learning methods, including CNN, VGG16, DenseNet121, and ResNet50. For this study, the disease was detected by using X-Ray images of patients with and without COVID-19 disease, and then it was evaluated how to increase the accuracy rate with the limited available data. To increase the accuracy rate, the results of data augmentation on the image data were examined and the time complexity of the algorithms with different layers was evaluated. As a result of the study, it was seen that data augmentation increased the performance rate in all algorithms and the ResNet50 algorithm was more successful than other algorithms. © 2022 IEEE.eninfo:eu-repo/semantics/restrictedAccessCNNCOVID-19 Deep LearningData AugmentationDenseNet121ResNet50VGG16conferenceObject2-s2.0-85133974292