Chest X-rays have been a subject of deep learning applications on medical images forthe high expectations of their evidence in assessing pulmonary diseases. Whereas thearising of COVID-19 or 2019 novel coronavirus in December 2019 have prioritizedresearch on pulmonary diseases diagnosis and prognosis, especially using artificialintelligence (AI) and Deep Learning (DL). For this sake, we extend the work ondetecting pneumonia using Faster Region-Based Convolutional Neural Networks(Faster R-CNN) by applying Faster R-CNN to the detection of Pneumonia and COVID-19 in Chest X-ray images using several datasets involving COVID-19 images. Differentcombinations of training scenarios in addition to internal and external testing atdifferent objectiveness thresholds, epochs counts, and lengths yielded variant results.Our results comply with the state of the art of Faster-RCNN in pneumonia detection butdo not show promising results in COVID-19 detection as a standalone model. Futurework may emphasize introducing segmentation to the model pipeline, adding asecondary classification stage, and even engaging other medical data to improve theperformance and constitute a robust Faster R-CNN-based prediction model.