Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

M. Fraiwan, L. Fraiwan, M. Alkhodari, O. Hassanin

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)4759-4771
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Volume13
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • Convolutional neural network
  • Deep learning
  • Long short-term memory
  • Lung sounds
  • Pulmonary diseases
  • Stethoscope

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