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<p>Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bulldog nostrils, including both real and AI-generated samples. Five neural network architectures were tested across three experiments, with DenseNet201 achieving the highest median F-score of 54.04%. The model’s performance was directly compared to trained human evaluators specializing in veterinary anatomy, achieving comparable levels of accuracy and reliability. These results demonstrate the potential of advanced neural networks to match human-level performance in diagnosis, paving the way for enhanced treatment planning and overall animal welfare.</p>

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Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月21日

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  • Gustavo da Silva Andrade,
  • Gabriel Toshio Hirokawa Higa,
  • Jarbas Felipe da Silva Ribeiro,
  • Joyce Katiuccia Medeiros Ramos Carvalho,
  • Wesley Nunes Gonçalves,
  • Marco Hiroshi Naka,
  • Hemerson Pistori

摘要

Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bulldog nostrils, including both real and AI-generated samples. Five neural network architectures were tested across three experiments, with DenseNet201 achieving the highest median F-score of 54.04%. The model’s performance was directly compared to trained human evaluators specializing in veterinary anatomy, achieving comparable levels of accuracy and reliability. These results demonstrate the potential of advanced neural networks to match human-level performance in diagnosis, paving the way for enhanced treatment planning and overall animal welfare.