Abstract
In recent years, deep learning models have improved how well various diseases, particularly respiratory
ailments, can be diagnosed. In order to assist in offering a diagnosis of respiratory pathologies in digitally
recorded respiratory sounds, this research will provide an evaluation of the effectiveness of several deep
learning models connected with the raw lung auscultation sounds in detecting respiratory pathologies. We
will also determine which deep learning model is most appropriate for this purpose. 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 %. We trained and
tested the model using a sound database made by the International Conference on Biomedical and Health
Informatics (ICBHI) in 2017 and annotated by experts with a classification of the lung sound.
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