AI-based COVID-19 disease detection in medical images: Advancements and implications in healthcare

Abstract

<p class="Keywords">Medical image analysis and categorization have seen success using artificial intelligence (AI) approaches and convolutional neural networks (CNNs). The diagnosis of COVID-19 based on the classification of chest X-ray images has been proposed in this research using a deep CNN architecture. Since there was no dataset of chest X-ray pictures that was sufficiently large and of high quality, it was difficult to execute a reliable and accurate CNN classification. The dataset is preprocessed utilizing several stages and procedures to build an acceptable training set for suggested CNN model to reach its optimal performance. This was carried out to address these complications, including the accessibility of a tiny, unbalanced dataset with poor photo quality. The datasets employed in this study included preprocessing processes such as medical image analysis, dataset balance, and data augmentation (DA). The simulation outcomes showed an accuracy of 99.80%, highlighting the strength of presented scheme in the specified application field. The comparative study used in the paper is conducted with a few ML algorithms that demonstrates the outperformance of the suggested scheme in comparison with other schemes in terms of various performance parameters. Additionally, two diagnostic tools, i.e., receiver operating characteristic (ROC) curve and precision-recall curve, that aid in the understanding of probabilistic forecast for binary (two-class) classification predictive modelling issues are also displayed in this article.</p>