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dc.contributor.authorIparraguirre-Villanueva, Orlandoes_ES
dc.contributor.authorGuevara-Ponce, Victores_ES
dc.contributor.authorRoque Paredes, Ofeliaes_ES
dc.contributor.authorSierra-Liñan, Fernandoes_ES
dc.contributor.authorZapata-Paulini, Joselynes_ES
dc.contributor.authorCabanillas-Carbonell, Michaeles_ES
dc.date.accessioned2023-01-25T15:18:29Z
dc.date.available2023-01-25T15:18:29Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/20.500.13053/7688
dc.description.abstract“Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.“es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherScience and Information Organizationes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_ES
dc.subject"Neural networks; transfer learning; pneumonia; detection; Convolutional"es_ES
dc.titleConvolutional Neural Networks with Transfer Learning for Pneumonia Detectiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.14569/IJACSA.2022.0130963es_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.publisher.countryGBes_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#3.03.00es_ES


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