RESUMEN
The newly detected Coronavirus pneumonia, dubbed COVID-19, is highly contagious and pathogenic. To combat this disease, the diagnostic step is mostly carried out utilizing the RT-PCR technique on nasopharyngeal and throat samples with sensitivity values ranging from 30 to 70%. Biomedical imaging, on the other hand, has sensitivity levels of 98 and 69 percent, respectively. In this paper, a machine learning model is built using convolutional neural networks (CNN) with 5 CNN architectures: VGG16, MobileNetV2, NASNetMobile, and ResNet-50. The presented model scored a precision rate of 81%, a recall rate of 72%, and an f1-score of 71%. Moreover, this research paper accommodates a proposed expansion to the existing model. The Expansion suggested is to create a lightweight version of the model for smartphones © 2021 ACM.