TY - JOUR
T1 - Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory
AU - Fraiwan, M.
AU - Fraiwan, L.
AU - Alkhodari, M.
AU - Hassanin, O.
N1 - Funding Information:
This research is supported by the Deanship of Scientific Research at Jordan University of Science and Technology, Jordan, grant No. 20210047, and the Office of Research and Sponsored Programs (ORSP) Abu Dhabi University’s, UAE.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/10
Y1 - 2022/10
N2 - In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z-score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases.
AB - In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z-score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases.
KW - Convolutional neural network
KW - Deep learning
KW - Long short-term memory
KW - Lung sounds
KW - Pulmonary diseases
KW - Stethoscope
UR - http://www.scopus.com/inward/record.url?scp=85103665510&partnerID=8YFLogxK
U2 - 10.1007/s12652-021-03184-y
DO - 10.1007/s12652-021-03184-y
M3 - Article
AN - SCOPUS:85103665510
SN - 1868-5137
VL - 13
SP - 4759
EP - 4771
JO - Journal of Ambient Intelligence and Humanized Computing
JF - Journal of Ambient Intelligence and Humanized Computing
IS - 10
ER -