Ozsoy, YarenTaskin, Deniz2024-06-122024-06-122021978-1-6654-2714-2https://doi.org/10.1109/ICEET53442.2021.9659627https://hdl.handle.net/20.500.14551/203537th International Conference on Engineering and Emerging Technologies (ICEET) -- OCT 27-28, 2021 -- Istanbul, TURKEYDue to the rapidly increasing population, workload is increasing in the field of health as well as in many different fields. In the increasing workload, there are models and algorithms developed to reduce the burden of our doctors by providing insight into the diagnosis of pneumonia. Early diagnosis of many diseases, especially pneumonia, is important in reducing the death rate. Therefore, diagnosing the disease with deep learning methods is an important development in medicine. In this study, AlexNet and GoogLeNet models were run and their performances were evaluated. Looking at the results, it turns out that the deep learning model is successful in diagnosing the presence of the disease. Open access data sets were used due to limited data in medicine. The data set consists of a total of 6357 chest x-rays. Accuracy, true positive rate, false positive rate and precision rates were determined by creating confusion matrix in the study.en10.1109/ICEET53442.2021.9659627info:eu-repo/semantics/closedAccessDeep LearningAlexnetGooglenetESAConference Object161163N/AWOS:0008281081000292-s2.0-85124645532N/A