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arXiv · 2303.08362

Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations

Hafsa Gulzar·Jiyun Li·Arslan Manzoor·Sadaf Rehmat·Usman Amjad·Hadiqa Jalil Khan
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Abstract

With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called Mel Frequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting-edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

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Hafsa Gulzar, Jiyun Li, Arslan Manzoor, Sadaf Rehmat, Usman Amjad, Hadiqa Jalil Khan. 2023-03-15. Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations. https://arxiv.org/abs/2303.08362

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