The emergence and spread of COVID-19 disease that is announced in December 2019 have impacted individuals’ economy and lives, as it causes respiratory diseases with various symptoms, including coughing and fever. Diagnosing COVID-19 is important to limit the spread of this virus and reduce the number of infections and deaths. The Real-Time Reverse Transcription– Polymerase Chain Reaction RT-PCRis a biology test used to diagnose COVID- 19; this test takes time to produce results. Also has another problem, sometimes the patient of COVID-19 gets a negative result in the RT-PCRtest. Because of these issues, there is a need for an alternative method that is faster and more accurate than the RT-PCR test. So, we are using the radiography method, which becomes necessary in this pandemic. Using x-rays for patients with clear symptoms will helps doctors to predict who are patients more infected and vulnerable to disease than others. An x-ray can display two-dimensional images of patients. We have collected chest x-ray images from five diverse datasets sources considered as large com- pared to previous studies. We used a deep learning technology as an alternative to the RT-PCR test. We suggested two ways to building a model. The first way developed a type of deep learning model by using a Convolutional Neural Network CNN. The model trained on chest x-ray images in the Visual Geometry Group (VGG-16) model. The second way was a hybrid model that is using features extraction from (VGG-16) and using other classification algorithms like support-vector-machines (SVM), Random-Forests (RF), and Extreme-Gradient-Boosting (XGBoost) to classified patients as normal or COVID-19. The result of a (VGG-16) model and a hybrid model that consists of (VGG16+SVM) was the same with 99.82% accuracy and 100% sensitivity (recall). All of the four models we suggested was reliable; the less accuracy of all of them was 98.73%.