Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

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 heal...

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Bibliographic Details
Main Authors: María Teresa García-Ordás, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides, Héctor Alaiz-Moretón
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sensors
Subjects:
cnn
variational autoencoder
respiratory
lungs
pathologies
Online Access:https://www.mdpi.com/1424-8220/20/4/1214

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https://www.mdpi.com/1424-8220/20/4/1214

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