RT info:eu-repo/semantics/article
T1 Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
A1 García Ordás, María Teresa
A1 Benítez Andrades, José Alberto
A1 García Rodríguez, Isaías
A1 Benavides Cuéllar, María del Carmen
A1 Alaiz Moretón, Héctor
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Ingeniería de sistemas
K1 Medicina. Salud
K1 Sociología
K1 CNN
K1 Pathologies
K1 Lungs
K1 Variational autoencoder
K1 Respiratory
K1 3308.09 Ingeniería Sanitaria
AB [EN] The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.
PB MDPI
LK https://hdl.handle.net/10612/18118
UL https://hdl.handle.net/10612/18118
NO García-Ordás, M. T., Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C., & Alaiz-Moretón, H. (2020). Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data. Sensors, 20(4), 1214. https://doi.org/10.3390/s20041214
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 08-may-2024