Sequential Modeling for Polyps Identification from the Vocal Data
Given the revival of neural networks and its recent impact in other disciplines and record-breaking performances in a variety of applications, in this paper, we employed a deep sequential model for polyps detection from the vocal data. Previous research of acoustic signal recognition (ASR) has focused on hand-crafted machine learning fashion, such as Mel-frequency cepstral coefficients with hidden Markov model and Gaussian mixture model. The deep model demonstrates its flexibility and potential to outperform the traditional methods, and we expand its scope on medical symptom identification. The mapping between the raw vocal signal and the symptom recognition is established, and we show that we can achieve a good recognition accuracy, which may appear to clinical diagnosis in the near future.
KeywordsVocal features Polyps Sequential model LSTM
This work was supported in part by NSFC under Grant 61771342, 61731006, 61711530132, and Tianjin Higher Education Creative Team Funds Program.
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