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This paper proposes a weakly-supervised machine learning-based approachaiming at a tool to alert patients about possible respiratory diseases. Varioustypes of pathologies may affect the respiratory system, potentially leading tosevere diseases and, in certain cases, death. In general, effective preventionpractices are considered as major actors towards the improvement of thepatient's health condition. The proposed method strives to realize an easilyaccessible tool for the automatic diagnosis of respiratory diseases.Specifically, the method leverages Variational Autoencoder architecturespermitting the usage of training pipelines of limited complexity and relativelysmall-sized datasets. Importantly, it offers an accuracy of 57 %, which is inline with the existing strongly-supervised approaches.

Variational Autoencoders for Anomaly Detection in Respiratory Sounds