Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations
This work addresses the need for automated diagnosis of respiratory sounds for medical professionals, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of diagnosing lung sound aberrations by developing a non-invasive technique using a CNN-based approach with transfer learning, achieving an accuracy of 95% and other metrics like precision of 88% on the ICBHI dataset.
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.