تقرير
Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations
| العنوان: | Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations |
|---|---|
| المؤلفون: | Gulzar, Hafsa, Li, Jiyun, Manzoor, Arslan, Rehmat, Sadaf, Amjad, Usman, Khan, Hadiqa Jalil |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: 12 pages, 9 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2303.08362 |
| رقم الأكسشن: | edsarx.2303.08362 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |