Abstract
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 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 a custom convolutional neural network (CNN) model and pre- trained deep neural architectures such as resnet50, DenseNet121, VGG16, and VGG19 to classify COVID-19 chest X-ray images. The proposed model has been evaluated on real-life data, and an accuracy of 98% has been achieved. The proposed model can be recommended to health care professionals as a trustworthy diagnostic decision-making system for COVID-19 detection.
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