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Large annotated lung sound databases are publicly available and might be usedto train algorithms for diagnosis systems. However, it might be a challenge todevelop a well-performing algorithm for small non-public data, which have onlya few subjects and show differences in recording devices and setup. In thispaper, we use transfer learning to tackle the mismatch of the recording setup.This allows us to transfer knowledge from one dataset to another dataset forcrackle detection in lung sounds. In particular, a single input convolutionalneural network (CNN) model is pre-trained on a source domain using ICBHI 2017,the largest publicly available database of lung sounds. We use log-melspectrogram features of respiratory cycles of lung sounds. The pre-trainednetwork is used to build a multi-input CNN model, which shares the same networkarchitecture for respiratory cycles and their corresponding respiratory phases.The multi-input model is then fine-tuned on the target domain of ourself-collected lung sound database for classifying crackles and normal lungsounds. Our experimental results show significant performance improvements of9.84% (absolute) in F-score on the target domain using the multi-input CNNmodel based on transfer learning for crackle detection in adventitious lungsound classification task.

Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks