ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings

• Published in: Artificial intelligence in medicine 2024-08, Vol.154, p.102922, Article 102922
• Main Authors: Majzoobi, Faezeh, Khodabakhshi, Mohammad Bagher, Jamasb, Shahriar, Goudarzi, Sobhan
• Format: Article
• Language: English
• Subjects: ConvLSTM; Convolutional neural network; Deep neural network; Long short-term memory; Multichannel lung sound recording
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2024.102922

Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states. By modeling the multichannel lung sounds through the ConvLSTM layer, the proposed ConvLSNet architecture can concurrently deal with the spatial and temporal properties of the six-channel LS recordings without heavy preprocessing or data transformation. Notably, the proposed model achieves a classification accuracy of 97.4 % based on LS data corresponding to three pulmonary conditions, namely asthma, COPD, and the healthy state. Compared with architectures consisting exclusively of CNN or LSTM layers, as well as those employing a cascade integration of 2DCNN and LSTM layers, the proposed ConvLSNet architecture exhibited the highest classification accuracy, while imposing the lowest computational cost as quantified by the number of parameters, training time, and learning rate. •Proposed ConvLSNet model for lung sound analysis.•Used ConvLSTM, 3DCNN, and LSTM layers to extract spatio-temporal features.•By exploiting the ConvLSTM layers the sequence-based properties of the LS encompass the whole 2D space across the lung.•Achieved 97.4% accuracy on asthma, COPD, and healthy states.•Outperformed the state-of-the-art models in terms of classification metrics, computational cost, and model size.