The COVID-19 is a virus that spreads quickly with a high mortality rate. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits and diagnosis using medical imaging methods have been investigated. Among the medical imaging tools, diagnosis with the help of Computed Tomography and X-ray images is very important. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In conclusion, the pre- trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. We believe that this study will help to defeat the epidemic.