Preliminary screenings are essential to limit the community propagating nature of COVID-19. COVID-19 patients’ lung health status can be assessed by chest radiographic imaging, such as computed tomography scanning or X-ray images. Therefore, it is preferable to use machine learning approaches to help identify COVID-19 by using chest radiographs. This research presents an image-based diagnosis of COVID-19 disease using Deep Learning. The presented work uses chest CXNet-A Novel approach for COVID-19 detection and Classification X-ray images because the X-ray imaging facility is widely available in almost all healthcare facilities. It is less costly and has a more negligible radiation effect than Computed Tomography (CT) scan images. This study employed the implemented a custom Convolutional Neural Network (CNN) model and pre-trained deep neural architecture such as resnet50, DenseNet121, VGG16, and VGG19 to classify COVID-19 chest X-ray images. The proposed model has been evaluated on the real-life data, the accuracy of 98% has been achieved. We can recommend the proposed model to health care professionals as a trustworthy diagnostic decision-making system for COVID-19 detection.