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<p>We propose a diagnostic system for identifying bronchial diseases by analyzing dog cough sounds. We collected a dataset consisting of 124 healthy dog cough sounds obtained from open sources and 94 dog cough sounds with bronchial diseases obtained from YouTube, and performed a total of 218 recordings. These cough sounds were segmented into 423 separate cough datasets to improve the details and accuracy of their analysis. Additionally, data augmentation techniques such as noise addition, pitch shifting, time stretching, and volume scaling were applied, increasing the dataset size by 7 times. This resulted in 1,526 training and testing samples for multiple coughs and 2,961 samples for single coughs. The disease prediction system leverages three different neural network models, multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN), to evaluate their effectiveness in detecting bronchial diseases. In our experiments, we found that the single cough dataset outperformed the multiple cough dataset, with the CNN achieving the highest accuracy, precision, AUC, and F1 scores compared to the RNN and MLP. The study highlights the potential of machine learning in improving diagnostic accuracy for veterinary medicine, suggesting that integrating different models could enhance diagnostic tools, thereby contributing to better health outcomes for dogs.</p>

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Dog Cough Sound Classification Using Neural Networks for Diagnosing Bronchial Diseases

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Do-Ye Kwon,
  • Yeon-Ju Oh,
  • Heewon Kim

摘要

We propose a diagnostic system for identifying bronchial diseases by analyzing dog cough sounds. We collected a dataset consisting of 124 healthy dog cough sounds obtained from open sources and 94 dog cough sounds with bronchial diseases obtained from YouTube, and performed a total of 218 recordings. These cough sounds were segmented into 423 separate cough datasets to improve the details and accuracy of their analysis. Additionally, data augmentation techniques such as noise addition, pitch shifting, time stretching, and volume scaling were applied, increasing the dataset size by 7 times. This resulted in 1,526 training and testing samples for multiple coughs and 2,961 samples for single coughs. The disease prediction system leverages three different neural network models, multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN), to evaluate their effectiveness in detecting bronchial diseases. In our experiments, we found that the single cough dataset outperformed the multiple cough dataset, with the CNN achieving the highest accuracy, precision, AUC, and F1 scores compared to the RNN and MLP. The study highlights the potential of machine learning in improving diagnostic accuracy for veterinary medicine, suggesting that integrating different models could enhance diagnostic tools, thereby contributing to better health outcomes for dogs.