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Convolutional Neural Network for Classifying Thoracic Diseases in Adult Patients

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XLVI Mexican Conference on Biomedical Engineering (CNIB 2023)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 96))

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Abstract

This paper proposes an automatic classifier, based on a convolutional neural network, capable of identifying different pathologies and diseases seen in anterior chest radiographs. The dataset was obtained from the National Institutes of Health (NIH) and Kaggle to be used as training for the classifier. For example, healthy, cardiomegaly, infiltration, effusion, mass, pneumothorax, emphysema, fibrosis, oedema, nodules and others. The average results for the evaluated metrics recall, accuracy and F1 score were 92%, 82% and 87% respectively, showing the flexibility of the proposed model to handle different tasks. This paper proposes an automatic classifier based on a convolutional neural network capable of identifying different pathologies and diseases shown in anterior chest X-ray studies. The data set was obtained from the National Institutes of Health (NIH) and Kaggle to be used as training for the classifier. E.g., Healthy, Cardiomegaly, Infiltration, Effusion, Mass, Pneumo-thorax, Emphysema, Fibrosis Edema, Nodule and others.

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Authors and Affiliations

  1. Universidad Autónoma de Chihuahua, Circuito Universitario 31109, Campus UACH II, 31125, Chihuahua, Mexico

    Luis Alberto Gómez-Celaya, Jorge Alonso Débora-Ramirez, Rolando Corral-Barrón, Poleth Rodríguez-Domínguez, Aldair Verdugo-Valerio, Carlos Eduardo Cañedo-Figueroa, Abimael Guzmán Pando & Hugo Varela-Rodríguez

Authors
  1. Luis Alberto Gómez-Celaya
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  2. Jorge Alonso Débora-Ramirez
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  3. Rolando Corral-Barrón
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  4. Poleth Rodríguez-Domínguez
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  5. Aldair Verdugo-Valerio
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  6. Carlos Eduardo Cañedo-Figueroa
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  7. Abimael Guzmán Pando
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  8. Hugo Varela-Rodríguez
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Corresponding author

Correspondence to Carlos Eduardo Cañedo-Figueroa .

Editor information

Editors and Affiliations

  1. División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Orizaba, Orizaba, Mexico

    José de Jesús Agustín Flores Cuautle

  2. Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA

    Balam Benítez-Mata

  3. Departamento de Bioingeniería Traslacional, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Ricardo Antonio Salido-Ruiz

  4. Facultad de Ingeniería, Universidad Autónoma de Guerrero, Chilpancingo de los Bravo, Guerrero, Mexico

    Gustavo Adolfo Alonso-Silverio

  5. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico

    Guadalupe Dorantes-Méndez

  6. Departamento de Ingeniería Eléctrica y Computación, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

    Esmeralda Zúñiga-Aguilar

  7. Departamento de Bioingeniería Traslacional, Universidad de Guadalajara, Guadalajara, Mexico

    Hugo A. Vélez-Pérez

  8. Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo, Mexico

    Edgar Del Hierro-Gutiérrez

  9. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico

    Aldo Rodrigo Mejía-Rodríguez

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Gómez-Celaya, L.A. et al. (2024). Convolutional Neural Network for Classifying Thoracic Diseases in Adult Patients. In: Flores Cuautle, J.d.J.A., et al. XLVI Mexican Conference on Biomedical Engineering. CNIB 2023. IFMBE Proceedings, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-031-46933-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-46933-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46932-9

  • Online ISBN: 978-3-031-46933-6

  • eBook Packages: EngineeringEngineering (R0)

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