An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 4595-4598, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-36086440
ABSTRACT
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.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2022
Tipo del documento:
Article