Identification of SARS-CoV-2 Pneumonia in Chest X-ray Images Using Convolutional Neural Networks

ABSTRACT

In 2019, COVID-19 disease emerged in Wuhan, China, leading to a pandemic that saturated health systems, raising the need to develop effective diagnostic methods. This work presents an approach based on artificial intelligence applied to X-ray images obtained from Mexican patients, provided by Hospital General de Zona No. 24. A dataset of 612 images with 2 classes COVID and HEALTHY, were labelled by a radiologist and also verified with positive RT-PCR test. The first class contains X-ray images of patients with pneumonia due to SARS-CoV-2 and the second contains patients without diseases affecting the lung parenchyma. The proposed work aims to classify COVID-19 pneumonia using convolutional neural networks to provide the physician with a suggestive diagnosis. Images were automatically trimmed and then transfer learning was applied to VGG-16 and ResNet-50 models, which were trained and tested using the generated dataset, both achieving an accuracy, recall, specificity and F1-score of over 98%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.