Mathematical evaluation of COVID-19 detection technique using CXR radiographs of patients, using CNN vggnet-16
NeuroQuantology
; 20(22):2575-2589, 2022.
Artículo
en Inglés
| EMBASE | ID: covidwho-2323908
ABSTRACT
The detection of COVID-19 by CXR imaging is a support tool for physicians and specialists since the pandemic and has been evolving rapidly because it provides early diagnosis, can be performed in any health center, and is more affordable than Real-Time Polymerase Chain Reaction (RT-PCR) tests. However, Chest X-Ray (CXR) imaging had not achieved the predictive capacity needed to replace the RT-PCR test;previous studies have evaluated their models with a limited amount of images. This study aims to contribute to the evaluation of a convolutional neural network (CNN) model to detect COVID-19 from CXR images, using open source and a free dataset containing approximately 30,000 images. The mathematical model or algorithm used was VGGNet-16. The results of the experiments show accuracy and precision of more than 95% and sensitivity, specificity, F1-measure,andthedictive ability of more than 90%.Copyright © 2022, Anka Publishers. All rights reserved.
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
EMBASE
Tipo de estudio:
Estudios diagnósticos
/
Estudio experimental
/
Estudio pronóstico
Idioma:
Inglés
Revista:
NeuroQuantology
Año:
2022
Tipo del documento:
Artículo
Similares
MEDLINE
...
LILACS
LIS