Deep Learning-Based Pneumonia Classification Based on Respiratory Sounds

Abstract

A crucial aspect of pneumonic pathology is attributed and analyzed by respiratory sound, which also provides information on the patient's lung symptoms.Almost all health institutions worldwide acquire pneumonia as a common and therapeutically challenging diagnosis that can lead to a severe body response to an infection, critical illness, and respiratory failure. It is well acknowledged that clinical diagnosis and prognosis are insufficient for the proper assessment of the severity of the disease due to the complexity of its pathogenesis. Auscultation and chest X-ray (CXR) images are currently the primary methods for identifying and diagnosing pneumonia, but it can be difficult for a trained radiologist to identify pneumonia from a CXR image or for a pathologist to do so using a stethoscope because accurate interpretation of respiratory sounds requires significant clinical expertise.

We attempted to create an automated classification of respiratory sounds to detect pneumonia as well as a more complicated classification to distinguish between different forms of pathology by using a deep learning convolutional neural network to classify the six classes (healthy, COPD, URTI, bronchiectasis, pneumonia, and bronchiolitis) of sounds recorded in a clinical setting. In the proposed method for pneumonia detection and pathological respiratory sound classification, a pertained picture feature extractor of series, respiratory sound, and CNN classifier are all used. The CNN model had an average precision of 98.8%, with a split among the classes of 97.80%, 87.60%, 95.0%, 85%, 100%, and 100% for COPD, healthy, URTI, bronchiectasis, pneumonia, and bronchiolitis, respectively. It detected abnormal sounds with an accuracy of 94.2% and pneumonia with an accuracy of 100%. The performance results obtained suggest that CNN is a viable tool for detecting specific characteristics in respiratory data and is capable of accurately classifying pneumonia.