Please use this identifier to cite or link to this item:
http://repositorio.ugto.mx/handle/20.500.12059/12488
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DC Field | Value | Language |
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dc.rights.license | http://creativecommons.org/licenses/by-nc-nd/4.0 | es_MX |
dc.creator | CARLOS EDUARDO BELMAN LOPEZ | es_MX |
dc.date.accessioned | 2024-08-12T19:22:13Z | - |
dc.date.available | 2024-08-12T19:22:13Z | - |
dc.date.issued | 2024-07-24 | - |
dc.identifier.issn | 2007-9621 | es_MX |
dc.identifier.uri | http://repositorio.ugto.mx/handle/20.500.12059/12488 | es_MX |
dc.description.abstract | Este documento presenta el diseño de una aplicación para detectar covid-19 utilizando redes neuronales convolucionales e imágenes de rayos X en dos escenarios (covid/No-covid y covid/Normal/Neumonía). Para evitar el sobreajuste, se utilizó aumento de datos, dropout, normalización por lotes y optimizador Adam. La red para tres clases se utilizó como modelo pre-entrenado ajustando solo la capa densa y de salida para obtener el modelo binario. Además, se realizó una optimización automatizada de hiper-parámetros como dropout, funciones de activación y número de neuronas. La tasa de aprendizaje se ajustó mediante callbacks para evadir óptimos locales. Las redes fueron convertidas al formato TensorFlow.js para integrarse en una aplicación híbrida utilizando Ionic y Capacitor, y se desplegaron mediante Firebase para brindar asistencia y soporte al generar diagnósticos. La aplicación obtuvo una exactitud del 98.61% y 96.48% para dos y tres clases, respectivamente, logrando mayor rendimiento que otras propuestas y utilizando menos parámetros de entrenamiento. | es_MX |
dc.language.iso | eng | en |
dc.relation | https://doi.org/10.15174/au.2024.3919 | es_MX |
dc.rights | info:eu-repo/semantics/openAccess | es_MX |
dc.source | Acta Universitaria: Multidisciplinary Scientific Journal. Vol. 34 (2024) | es_MX |
dc.title | Design of an application to detect covid-19 using convolutional neural networks and X-ray images | en |
dc.title.alternative | Diseño de una aplicación para detectar covid-19 mediante redes neuronales convolucionales e imágenes de rayos X | es_MX |
dc.type | info:eu-repo/semantics/article | es_MX |
dc.creator.id | info:eu-repo/dai/mx/cvu/773443 | es_MX |
dc.subject.cti | info:eu-repo/classification/cti/3 | es_MX |
dc.subject.cti | info:eu-repo/classification/cti/32 | es_MX |
dc.subject.cti | info:eu-repo/classification/cti/3201 | es_MX |
dc.subject.keywords | Covid-19 | en |
dc.subject.keywords | Convolutional neural networks | en |
dc.subject.keywords | X-ray | en |
dc.subject.keywords | Adaptive Moment Estimation (Adam) | en |
dc.subject.keywords | Computer application | en |
dc.subject.keywords | Redes neuronales convolucionales | es_MX |
dc.subject.keywords | Rayos X | es_MX |
dc.subject.keywords | Estimación Adaptativa de Momentos (Adam) | es_MX |
dc.subject.keywords | Aplicación informática | es_MX |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_MX |
dc.publisher.university | Universidad de Guanajuato | es_MX |
dc.description.abstractEnglish | This research presents the design of an application to detect covid-19 using convolutional neural networks and X-ray images in two scenarios (covid/Non-covid and covid/Normal/Pneumonia). To avoid overfitting online data augmentation, dropout, batch normalization, and Adam optimizer was used. The three-class network was used as a pre-trained model, tuning only the dense and output layers to obtain the binary model. Additionally, hyper-parameter optimization was used to get dropout probabilities, activation functions, and neurons. The learning rate was adjusted using callbacks to avoid local optimums. Networks were converted to TensorFlow.js format and embedded locally in a hybrid application using Ionic and Capacitor and were deployed through Firebase to help provide diagnostics. The application obtained an accuracy of 98.61% and 96.48% for two and three classes, respectively, achieving higher performance when compared to other proposals (offline models) in the literature and using fewer training parameters. | en |
Appears in Collections: | Revista Acta Universitaria |
Files in This Item:
File | Description | Size | Format | |
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Design of an application to detect covid-19 using convolutional neural networks and X-ray images.pdf | 1.75 MB | Adobe PDF | View/Open |
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