TY - GEN
T1 - Identification of SARS-CoV-2 Pneumonia in Chest X-ray Images Using Convolutional Neural Networks
AU - Delena-García, Paola I.
AU - Torres-Rodríguez, José D.
AU - Tovar-Corona, Blanca
AU - Anzueto-Ríos, Álvaro
AU - Fragoso-Olvera, Nadia L.
AU - Flores-Patricio, Alberto
AU - Camarillo-Nava, Victor M.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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%.
AB - 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%.
KW - Artificial Intelligence
KW - COVID-19
KW - Chest X-ray
KW - Convolutional neural networks
KW - Pneumonia
UR - http://www.scopus.com/inward/record.url?scp=85142693150&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18082-8_10
DO - 10.1007/978-3-031-18082-8_10
M3 - Contribución a la conferencia
AN - SCOPUS:85142693150
SN - 9783031180811
T3 - Communications in Computer and Information Science
SP - 157
EP - 172
BT - Telematics and Computing - 11th International Congress, WITCOM 2022, Proceedings
A2 - Mata-Rivera, Miguel Félix
A2 - Zagal-Flores, Roberto
A2 - Barria-Huidobro, Cristian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Congress of Telematics and Computing, WITCOM 2022
Y2 - 7 November 2022 through 11 November 2022
ER -