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dc.contributor.author Sanchez, Jessica
dc.contributor.author Trueba, Adrian
dc.contributor.author Cervantes, Jair
dc.contributor.author Ruiz Castilla, Jose Sergio
dc.contributor.author Garcia Lamont, Farid
dc.date.accessioned 2024-08-06T20:25:35Z
dc.date.available 2024-08-06T20:25:35Z
dc.date.issued 2024-06-08
dc.identifier.issn 1577-5097
dc.identifier.uri http://hdl.handle.net/20.500.11799/141006
dc.description.abstract In this paper we propose the classification of radiological patterns with the presence of tuberculosis in X-ray images, it was observed that two to six patterns (consolidation, fibrosis, opacity, opacity, pleural, nodules and cavitations) are present in the radiographs of the patients. It is important to mention that species specialists consider the type of TB pattern in order to provide appropriate treatment. It should be noted that not all medical centres have specialists who can immediately interpret radiological patterns. Considering the above, the aim is to classify patterns by means of a convolutional neural network to help make a more accurate diagnosis on X-rays, so that doctors can recommend immediate treatment and thus avoid infecting more people. For the classification of tuberculosis patterns, a proprietary convolutional neural network (CNN) was proposed and compared against the VGG16, InceptionV3 and ResNet-50 architectures, which were selected based on the results of other radiograph classification research [1]–[3] . The results obtained for the Macro-averange AUC-SVM metric for the proposed architecture and InceptionV3 were 0.80, and for VGG16 it was 0.75, and for the ResNet-50 network it was 0.79. The proposed architecture has better classification results, as does InceptionV3. es
dc.language.iso eng es
dc.publisher Electronic Letters on Computer Vision and Image Analysis es
dc.rights openAccess es
dc.rights.uri http://creativecommons.org/licenses/by/4.0 es
dc.subject Tuberculosis patterns es
dc.subject Convolutional neural networks es
dc.subject Chest X-rays es
dc.subject.classification INGENIERÍA Y TECNOLOGÍA es
dc.title Classification of radiological patterns of tuberculosis with a convolutional neural network in x-ray images es
dc.type Artículo es
dc.provenance Científica es
dc.road Dorada es
dc.organismo Centro Universitario UAEM Texcoco es
dc.ambito Internacional es
dc.cve.CenCos 30401 es
dc.cve.progEstudios 1009 es
dc.relation.vol 23
dc.relation.año 2024
dc.relation.no 1
dc.relation.doi 10.5565/rev/elcvia.1822


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  • Título
  • Classification of radiological patterns of tuberculosis with a convolutional neural network in x-ray images
  • Autor
  • Sanchez, Jessica
  • Trueba, Adrian
  • Cervantes, Jair
  • Ruiz Castilla, Jose Sergio
  • Garcia Lamont, Farid
  • Fecha de publicación
  • 2024-06-08
  • Editor
  • Electronic Letters on Computer Vision and Image Analysis
  • Tipo de documento
  • Artículo
  • Palabras clave
  • Tuberculosis patterns
  • Convolutional neural networks
  • Chest X-rays
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

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