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Pneumonia is an infectious disease that has afflicted humanity for centuries. Its origins can be diverse, such as bacterial, viral, fungal, or chemical agents. It is one disease that causes the most deaths among children and adults worldwide. There are many ways to treat pneumonia, however, it is a fact that the sooner it is detected, the greater the chances of successful treatment. Therefore, it is right to think that developing ways to make the diagnosis faster, and facilitating early treatment, is of general interest. Therefore, the present work presents a neural network for the analysis of chest radiographs for the diagnosis of pneumonia. The proposed method showed remarkable results when compared to similar methods in the literature. In addition, the proposed method presents a more transparent diagnosis through relevance aggregation, highlighting the regions of images that were recognized by the neural network to perform the diagnosis, also contributing to interpretable results.

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A Simple and Interpretable Deep Learning Model for Diagnosing Pneumonia from Chest X-Ray Images

  • 会议论文

  • 发表日期：

    2025年

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  • Lucas Otavio Leme Silva,
  • Karine Marques Hara,
  • Pedro Henrique Mendes de Paula,
  • Alexandre Rossi Paschoal,
  • Fabricio Martins Lopes

摘要

Pneumonia is an infectious disease that has afflicted humanity for centuries. Its origins can be diverse, such as bacterial, viral, fungal, or chemical agents. It is one disease that causes the most deaths among children and adults worldwide. There are many ways to treat pneumonia, however, it is a fact that the sooner it is detected, the greater the chances of successful treatment. Therefore, it is right to think that developing ways to make the diagnosis faster, and facilitating early treatment, is of general interest. Therefore, the present work presents a neural network for the analysis of chest radiographs for the diagnosis of pneumonia. The proposed method showed remarkable results when compared to similar methods in the literature. In addition, the proposed method presents a more transparent diagnosis through relevance aggregation, highlighting the regions of images that were recognized by the neural network to perform the diagnosis, also contributing to interpretable results.