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    Area of Science:

    • Medical acoustics
    • Signal processing
    • Artificial intelligence in healthcare

    Background:

    • Medical acoustics is an emerging field focused on automated analysis of medical signals.
    • Accurate classification of respiratory sounds is crucial for clinical decision-making.

    Purpose of the Study:

    • To develop a framework for distinguishing between normal and abnormal respiratory sounds.
    • To leverage machine learning for enhanced respiratory sound analysis.

    Main Methods:

    • A multiresolution analysis-based feature set was designed to capture respiratory sound structures.
    • A Siamese Neural Network was employed to learn relationships between sound pairs.
    • The framework addresses class imbalance by training on similar/dissimilar sound pairs.

    Main Results:

    • The proposed framework demonstrated superior performance compared to existing methods.
    • The approach effectively handles the class imbalance inherent in respiratory sound datasets.
    • Explainable predictions were achieved through an interactive Q&A scheme.

    Conclusions:

    • The developed framework offers a robust and accurate method for respiratory sound classification.
    • The Siamese Neural Network approach provides a promising solution for medical acoustics applications.
    • The explainable AI component facilitates trust and collaboration between AI systems and medical experts.