LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation

Ma, Yi; Xu, Xinzi; Li, Yongfu

doi:10.21437/interspeech.2020-2487

Cited by 54 publications

(33 citation statements)

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“…of normal breathing sounds and various types of adventitious sounds [38,[46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. The models in most of these studies are developed on the basis of an open-access ICBHI database [20,21].…”

Section: Plos Onementioning

confidence: 99%

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

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Section: Plos Onementioning

confidence: 99%

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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show abstract

“…At the same time, Ma et al [ 28 ] introduced a non-local (NL) block into a ResNet and used STFT features for lung sound classification. Yang et al [ 29 ] analyzed STFT features with a ResNet with squeeze and excitation (SE) and spatial attention (SA) blocks for the identification of abnormal lung sounds, while another study by Pham et al [ 30 ] implemented a mixture-of-experts (MoE) block into a CNN structure and used mel spectrogram, gammatone-based spectrogram, MFCC and rectangular constant Q transform (CQT) features for the same purpose.…”

Section: Related Workmentioning

confidence: 99%

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Petmezas

Cheimariotis

Stefanopoulos

et al. 2022

Sensors

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Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.

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“…The performance was 54% of average score on the official ICBHI dataset. Ma et al proposed two ALSC systems for four classes [42], [43]. The first one used an improved Bi-ResNet deep learning architecture based on STFT The proposed systems achieved 50.16% and 52.26% on the official data split, respectively.…”

Section: A Lung Sound Classification On Icbhi Datasetmentioning

confidence: 99%

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Nguyen¹,

Pernkopf²

2021

Preprint

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In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pretrained model is transferred by using vanilla fine-tuning, cotuning, stochastic normalization and the combination of the cotuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.

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LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation

Cited by 54 publications

References 0 publications

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Lung Sound Classification Using Co-tuning and Stochastic Normalization

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