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

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.

The online version supplementary material available at 10.1007/s12652-021-03184-y.