  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.
