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Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14196))

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Abstract

Chest radiography exams are still one of the main methods for detecting and diagnosing certain thoracic pathologies. This study evaluates the performance of a DenseNet in a multi-label classification task on radiography images, using focal loss as the loss function to address the class imbalance problem. For the experiments, 14 different types of findings were considered. Satisfactory results were obtained using the area under the ROC curve (AUC-ROC) as the metric, where the average performance across all classes was 0.861.

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Authors and Affiliations

  1. Núcleo de Computacão Aplicada, Universidade Federal do Maranhão (UFMA) Caixa Postal, 65.085-580, São Luís, MA, Brazil

    Alison Corrêa Mendes, Alexandre César Pinto Pessoa & Anselmo Cardoso de Paiva

Authors
  1. Alison Corrêa Mendes
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  2. Alexandre César Pinto Pessoa
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  3. Anselmo Cardoso de Paiva
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Correspondence to Alison Corrêa Mendes .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Mendes, A.C., Pessoa, A.C.P., de Paiva, A.C. (2023). Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_12

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  • DOI: https://doi.org/10.1007/978-3-031-45389-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45388-5

  • Online ISBN: 978-3-031-45389-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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