Utilization of Deep Learning Models for the Early Diagnosis of COVID-19 through Chest Radiography

Abstract

This study aims to evaluate the earlier detection of COVID-19 through deep-learning chest radiography models. The discussion starts with the impact of SARS-CoV-2 globally and the demand for early indication of the risk of handling infection. It outlines pathogenesis and clinical features of COVID-19, puts forth shortcomings of conventional diagnostics, and elaborates on its increasing role as a choice of preferred diagnostic technique in the form of chest radiography. In this study, a deep residual learning-based convolutional neural network architecture of 49 layers is proposed and its performance is compared with several advanced transfer learning models, like ResNet-50, VGG-16, VGG-19, and Inception-v3. The dataset used the chest radiographs of the patients of the COVID-19,for whom diverse enhancement techniques such as data augmentation, resolution optimization, and region of interest (ROI) selection were applied to improve the model's diagnostic performance. The methodology section describes preparation and preprocessing of the dataset followed by the proposed CNN model configuration that aims to evaluate the models concerning the performance metrics of accuracy, sensitivity, specificity, and F1-score. Comparative results show that the 49-layer CNN obtained an accuracy at the value of 97.0%, sensitivity at 98.41%, specificity at 88.45%, and an F1-score of 92.22%. This reflects that the proposed model outperformed the best-known state-of-the-art methods. The study places great emphasis on diagnostic approaches not only towards medical diagnosis but more so onto the transformative potential in redefining AI integration into healthcare systems as forces of change in addressing health matters such as the COVID-19 pandemic in global health.