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Respiratory diseases represent a major cause of morbidity and mortality worldwide, highlighting the need for rapid and accurate diagnostic tools. This article introduces an innovative method for the automatic detection of wheezes based on a convolutional neural network (CNN) model. By leveraging a database of respiratory sound recordings and advanced feature extraction techniques such as Mel-frequency cepstral coefficients (MFCC), our system achieves a 93% accuracy in classifying respiratory conditions. This approach holds great promise for significantly improving current diagnostic tools by combining efficiency, accuracy, and accessibility.

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Automatic Classification of Respiratory Sounds for Wheeze Detection Using Artificial Intelligence

  • 会议论文

  • 发表日期：

    2026年

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  • Boutaina Bellaysar,
  • Ouafaa Ikhzmane,
  • Fatima Ezahra Mouas,
  • Nour Akalai,
  • Lamyae Chentoufi,
  • Achraf Benba

摘要

Respiratory diseases represent a major cause of morbidity and mortality worldwide, highlighting the need for rapid and accurate diagnostic tools. This article introduces an innovative method for the automatic detection of wheezes based on a convolutional neural network (CNN) model. By leveraging a database of respiratory sound recordings and advanced feature extraction techniques such as Mel-frequency cepstral coefficients (MFCC), our system achieves a 93% accuracy in classifying respiratory conditions. This approach holds great promise for significantly improving current diagnostic tools by combining efficiency, accuracy, and accessibility.