Abnormal lung sounds include crackles, wheezes, rhonchi, stridor, and pleural friction rubs (Table 1). Recent electronic stethoscopes have rendered lung sounds recordable, and it facilitated the studies of automatically analyzing lung sounds 4, 5.

Auscultation is non-invasive, real-time, inexpensive, and very informative 1, 2, 3. The stethoscope has been considered as an invaluable diagnostic tool ever since it was invented in the early 1800s. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. To overcome such limitations, we tried to develop an automated classification of breath sounds. However, accurate interpretation of respiratory sounds requires clinician’s considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. Auscultation has been essential part of the physical examination this is non-invasive, real-time, and very informative.

Amplitude of lung sounds