Mathematical Evaluation of Covid-19 Detection Technique Using Cxr Radiographs of Patients, Using Densenet-201
NeuroQuantology
; 20(22):2590-2602, 2022.
Artículo
en Inglés
| EMBASE | ID: covidwho-2323909
ABSTRACT
A current COVID-19 detection tool is CXR imaging, which has been developing since 2019 to provide early diagnosis;it can be performed in any health unit and is more affordable than Real Time Polymerase Chain Reaction (RT-PCR) tests. However, diagnosis with Chest X Ray (CXR) images had not achieved the predictive capacity required to replace the RT-PCR test;previous studies with a limited number of images have evaluated their models. This research seeks to contribute to the detection of COVID-19 from CXR images, with the evaluation of a convolutional neural network model from CXR images, through the use of open source code on a free dataset of approximately 30 thousand images. The algorithm and mathematical model used was DenseNet-201. The results of the experiment show a precision and accuracy of more than 95% and specificity, sensitivity, predictive ability and F1 measurement 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
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