Early identification of different pulmonary pathologies using chest X-ray and machine learning algorithms

Mena Urresta, Kevin Andrés

Please use this identifier to cite or link to this item: http://repositorio.yachaytech.edu.ec/handle/123456789/783

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DC Field	Value	Language
dc.contributor.advisor	Almeida Galárraga, Diego Alfonso	-
dc.contributor.advisor	Gudiño Gomezjurado, Marco Esteban	-
dc.contributor.author	Mena Urresta, Kevin Andrés	-
dc.date.accessioned	2024-05-31T19:46:09Z	-
dc.date.available	2024-05-31T19:46:09Z	-
dc.date.issued	2024-05	-
dc.identifier.uri	http://repositorio.yachaytech.edu.ec/handle/123456789/783	-
dc.description	The present research work focuses on the study of several neural networks trained for the detection of lung diseases. The networks used were ResNet-18, ResNet-50, ResNet 101, VGG19 and Inception V3 and the data set was obtained from NIHCC being 112120 the total number of images with which different experiments and optimization were performed to obtain better results from the training of the networks.	es
dc.description.abstract	El presente trabajo de investigación se centra en el estudio de varias redes neuronales entrenadas para la detección de enfermedades pulmonares. Las redes utilizadas fueron ResNet-18, ResNet-50, ResNet 101, VGG19 e Inception V3 y la base de datos fue obtenida de NIHCC siendo 112120 el número total de imágenes con las que se realizaron distintos experimentos y optimización para obtener mejores resultados de los entrenamientos de las redes.	es
dc.language.iso	eng	es
dc.publisher	Universidad de Investigación de Tecnología Experimental Yachay	es
dc.rights	openAccess	es
dc.subject	Enfermedades pulmonares	es
dc.subject	Redes neuronales	es
dc.subject	Clasificación de imágenes	es
dc.subject	Pulmonary diseases	es
dc.subject	Neural networks	es
dc.subject	Image classification	es
dc.title	Early identification of different pulmonary pathologies using chest X-ray and machine learning algorithms	es
dc.type	bachelorThesis	es
dc.description.degree	Ingeniero/a Biomédico/a	es
dc.pagination.pages	91 hojas	es
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