Automated Detection of Pulmonary Diseases From Lung Sound Signals Using Fixed-Boundary-Based Empirical Wavelet Transform

Interpretation of lung disease classification with light attention connected module

“…We summarized the performance results in comparison with those in [51] , [52] in Table 14 . The methodology proposed achieved high performance with an accuracy of 86.37 % ( SD 4.02).…”

“…The methodology proposed achieved high performance with an accuracy of 86.37 % ( SD 4.02). In [51] , the Mahalanobis distribution and ResNet were used, and in [52] , the features were extracted through empirical wavelet transformation, and the respiratory sounds were classified using a light gradient boosting machine (LGBM). The performance of the proposed methodology was the highest in the experimental results, and the effects of attention were confirmed.…”

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

“…The findings demonstrated that the proposed method could perform reasonable arrhythmia detection with good accuracy. Tripathyet al [15] proposed a WT based disease detection classification method for the problem of lung disease detection. The process fixed the boundary points and extracted the frequency domain features from each disease pattern.…”

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

“…The results show that their highest performance was obtained using 60–40 training and testing sets for two classes problem using Logmel Spectrogram, ResNet101 and scored specificity of 91.77% and sensitivity of 95.76%. Moreover, Tripathy et al ( 2022 ) proposed a methodology using empirical wavelet transform with fixed boundary points. Where, the time-domain (Shannon entropy) and frequency-domain (peak amplitude and peak frequency) features have been extracted.…”

“…Then, machine learning classifiers, such as support-vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), have been chosen to detect PDs using the features of LS signals automatically. The performance of these features shows a promising result and can be enhanced further for multi-class scenarios like normal versus asthma, normal versus pneumonia, normal versus chronic obstructive pulmonary disease (COPD), and normal versus pneumonia versus asthma versus COPD classification schemes (Tripathy et al 2022 ).…”