Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel architecture called noise masking recurrent neural network (NMRNN) for lung sound classification. The model jointly learns to extract only important respiratory-like frames without redundant noise and then by exploiting this information is trained to classify lung sounds into four categories: normal, containing wheezes, crackles and both wheezes and crackles. We compare the performance of our model with machine learning based models. As a result, the NMRNN model reaches state-of-the-art performance on recently introduced publicly available respiratory sound database. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_21 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Respiratory sound classification, Recurrent neural networks, Deep learning | Crackles,Respiratory sounds,Masking (art),Pattern recognition,Computer science,Recurrent neural network,Artificial intelligence,Deep learning,Lung sound | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 4 |
PageRank | References | Authors |
0.47 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kirill Kochetov | 1 | 4 | 0.47 |
| Evgeny Putin | 2 | 4 | 0.47 |
| Maksim Balashov | 3 | 4 | 0.47 |
| Andrey Filchenkov | 4 | 46 | 15.80 |
| Anatoly Shalyto | 5 | 98 | 20.06 |