| Autor: |
Lam, Pham, Dat, Ngo, Khoa, Tran, Truong, Hoang, Alexander, Schindler, Ian, McLoughlin |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). |
| DOI: |
10.1109/embc48229.2022.9871440 |
| Popis: |
This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles collected from patients are transformed into time-frequency spectrograms to serve as front-end two-dimensional features. Cropped spectrogram segments are then used to train a range of back-end deep learning networks to classify respiratory cycles into predefined medically-relevant categories. A set of those trained high-performance deep learning frameworks are then fused to obtain the best score. Our experiments on the ICBHI benchmark dataset achieve the highest ICBHI score to date of 57.3%. This is derived from a late fusion of inception based and transfer learning based deep learning frameworks, easily outperforming other state-of-the-art systems. Clinical relevance--- Respiratory disease, wheeze, crackle, inception, convolutional neural network, transfer learning. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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