earticle

영어

Since the onset of the Coronavirus outbreak in December 2019, the virus has infected over six hundred million individuals, resulting in more than six million confirmed deaths, as reported by the World Health Organization (WHO). COVID- 19 is attributed to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and is recognized as a respiratory ailment, characterized by symptoms including fever, myalgia, dry cough, headache, sore throat and chest pain. As of October 2022, substantial efforts have been directed toward understanding and combatting the disease, particularly in the domains of vaccination and diagnosis. This paper focuses on the diagnosis of COVID-19 using X-ray images and leverages deep learning technologies. Specifically, we concentrate on employing three convolutional neural network models: ResNet50, InceptionV3 and MobileNetV2. The primary objective is to evaluate their performance in diagnosing COVID-19 from Xray images. During our research, we subjected these models to testing with unseen data. The results revealed that ResNet50 achieved an accuracy of 82.5%, outperforming InceptionV3 with 62.5% and MobileNetV2 with 65% accuracy. The adoption of these models not only alleviates the decision-making burden on medical experts but also enhances the precision of disease classification. The significance of this study lies in its contribution to fine-tuning diagnostic algorithms, paving the way for further research and advancements in the field.