 This paper proposes two frameworks for automated COVID-19 screening. The first model uses a convolutional neural network, CNN, as a feature extractor and XG boost as a classifier. The second model employs a CNN architecture combined with a feed-forward neural network, FFN. The hybrid model achieved high precision, 98.43%, recall, 98.41%, specificity, 99.26%, accuracy, 99.04%, an F1 score, 98.42%. The standalone CNN model had slightly lower but still commendable performance, with precision, 98.25%, recall, 98.44%, specificity, 99.27%, accuracy, 98.97%, an F1 score, 98.34%. These models outperformed five other state of. This article was authored by Afalil Dumakud and Absulm Iizagu. We are article.tv, links in the description below.