Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model

Sri Venkatesa Perumal college of Engineering and Technology

Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten peoples lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The article contains an approach for removing the noise that is very difficult to filter but the removal is crucial for identifying the respiratory phases. Finally, the respiratory phases are overlaid with the frequency spectrum which simplifies the orientation in the recording and additionally offers the information on the inter- individual ratio of the inhalation and exhalation phases. Such interpretation provides a powerful tool for further analysis of lung sounds, simplify the diagnosis of various types of respiratory tract dysfunctions, and returns data which are comparable among the patients.

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