The presented model was able to classify lung sounds into three categories: healthy, chronic obstructive pulmonary disease (COPD), and other diseases with an accuracy of 97%, which shows the superiority of the proposed method compared to results obtained from previous methods on the same database. Data used in this article is from the ICBHI lung sound database. These sounds can also occur if a foreign object like food obstructs the airway. This includes asthma, emphysema, pneumonia, acute bronchitis, chronic bronchitis, congestive heart failure, and more. Decreased breath sounds when the patient is making a phenomenal effort to breathe (with hyper-resonance) is the most important physical finding for emphysema.

The audio data sequence is investigated by adding the temporal attention mechanism and the bi-directional recurrent gates for weighting data according to its informational value. Many diseases and conditions can cause abnormal breath sounds. In this article, we propose a model which additionally employs a recurrent representation (Recurrence plot) that reflects the temporal characteristics of the sound. After several preprocessing steps (wavelet smoothing, displacement artifact removal, and z-sore normalization), two deep learning network architectures including CNN and bidirectional long short-term. Chronic bronchitis is defined clinically as the presence of a chronic. The result is airflow obstruction that is not fully reversible. Chronic obstructive lung disease is a disorder in which subsets of patients may have dominant features of chronic bronchitis, emphysema, or asthma. In previous studies, models have been proposed that feed spectrograms with frequency characteristics as inputs to the convolutional network. conducted a study to explore the ability of deep learning algorithms in recognizing pulmonary diseases from recorded lung sounds. Venn diagram of chronic obstructive pulmonary disease (COPD).

Lung infections can cause serious problems in people with COPD. we used a convolutional network to attain the advantages of transfer learning. Prevention and treatment of lung infections. Development of accessible methods based on machine learning can aid with early diagnosis. Diagnostic methods based on audio signal processing are non-invasive and inexpensive and can be especially useful in under-developed countries, where inadequate medical specialists and equipment has led to high fatality rates.

Analysis and examination of sound of organs can be utilized in order to diagnose various diseases and abnormal conditions.

Copd lung sounds