Using Artificial Intelligence and X-ray Images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment
Journal
Communications in Computer and Information Science
Information and Communication Technologies
ISSN
1865-0929
1865-0937
Date Issued
2025
Author(s)
Bryan Juárez-Gonzalez
Fernando Villalba-Meneses
Jonathan Cruz-Varela
Andrés Tirado-Espín
Paulina Vizcaino-Imacaña
Carolina Cadena-Morejon
Diego Almeida-Galárraga
Type
book-chapter
Abstract
In January 2022, Ecuador experienced a peak in COVID-19 cases, with 890,541 confirmed cases and 35,658 deaths. During 2019-2020, influenza and pneumonia also ranked among the top causes of death. Traditional chest X-rays and chest CT scans are commonly used for diagnosing COVID-19, but studies by Wong et al. indicate that chest X-rays are less sensitive compared to CT scans unless artificial intelligence is utilized. Tahir et al. highlighted that AI models such as U-net and neural networks achieve high diagnostic accuracy, with U-Net++ and ResNet18 models showing sensitivities above 99% and perfect specificity using a large dataset of 33,920 chest X-ray images. The rapid detection of symptoms could have helped in prioritizing critical care, potentially reducing deaths. In the current study, it is demonstrated that combining AI with chest X-rays can achieve a binary accuracy above 98% for COVID-19 detection using transfer learning with Xception, VGG16, and VGG19 neural networks. Using 27,052 chest X-ray images, the VGG19 model achieved an excellent F1-score of 98.53% for COVID-19 and normal class classification. The VGG19 model also performed well in multiclass classification with an F1-score above 89%. The study concludes that AI-enhanced diagnostic tools, such as VGG19, are valuable for hospitals in diagnosing COVID-19 and future improvements might include larger datasets and enhanced segmentation techniques.