Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals

Dar, Jawad Ahmad; Srivastava, Kamal Kr; Lone, Sajaad Ahmed

doi:10.1016/j.bspc.2022.103905

Cited by 17 publications

(3 citation statements)

References 26 publications

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“…In the world of medicine, the deep learning methodology is effective for the process of disease detection and categorization. The disease Covid-19 and brain tumors were identified using Deep Convolutional Neural Network (DCNN) [ 29 , 30 ]. ANN functions are synonyms for the brain of a human being.…”

Section: Methodsmentioning

confidence: 99%

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Dagne,

Palanivel

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Dagne,

Palanivel

et al. 2023

Heliyon

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“…Compared with transfer learning, they showed an accuracy of more than 96.17 %. Dar et al [13] extracted the main features of respiratory sounds using the bark frequency cepstral coefficient, spectral flux, and spectral centroid. They proposed a hierarchical attention network structure that combined a CNN and LSTM.…”

Section: Related Workmentioning

confidence: 99%

“…There is a limit to finding abnormal breathing characteristics if the patterns are complex or many. Several systems have been used to solve problems, but these systems have limitations [13] , [14] . Therefore, this study classified breathing patterns after preprocessing using a bandpass filter to solve the nonlinear pattern problem and misdiagnosis of lung diseases.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of lung disease classification with light attention connected module

Choi

Lee

2023

Biomedical Signal Processing and Control

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Deep learning and feature fusion-based lung sound recognition model to diagnoses the respiratory diseases

Shehab,

Mohammed,

Darwish

et al. 2024

Soft Comput

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This paper proposed a novel approach for detecting lung sound disorders using deep learning feature fusion. The lung sound dataset are oversampled and converted into spectrogram images. Then, extracting deep features from CNN architectures, which are pre-trained on large-scale image datasets. These deep features capture rich representations of spectrogram images from the input signals, allowing for a comprehensive analysis of lung disorders. Next, a fusion technique is employed to combine the extracted features from multiple CNN architectures totlaly 8064 feature. This fusion process enhances the discriminative power of the features, facilitating more accurate and robust detection of lung disorders. To further improve the detection performance, an improved CNN Architecture is employed. To evaluate the effectiveness of the proposed approach, an experiments conducted on a large dataset of lung disorder signals. The results demonstrate that the deep feature fusion from different CNN architectures, combined with different CNN Layers, achieves superior performance in lung disorder detection. Compared to individual CNN architectures, the proposed approach achieves higher accuracy, sensitivity, and specificity, effectively reducing false negatives and false positives. The proposed model achieves 96.03% accuracy, 96.53% Sensitivity, 99.424% specificity, 96.52% precision, and 96.50% F1 Score when predicting lung diseases from sound files. This approach has the potential to assist healthcare professionals in the early detection and diagnosis of lung disorders, ultimately leading to improved patient outcomes and enhanced healthcare practices.

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Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals

Cited by 17 publications

References 26 publications

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Interpretation of lung disease classification with light attention connected module

Deep learning and feature fusion-based lung sound recognition model to diagnoses the respiratory diseases

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