Variational Autoencoders for Anomaly Detection in Respiratory Sounds

• URL: http://arxiv.org/abs/2208.03326v2
• Date: Sun, 3 Dec 2023 11:03:58 GMT
• Title: Variational Autoencoders for Anomaly Detection in Respiratory Sounds
• Authors: Michele Cozzatti, Federico Simonetta, Stavros Ntalampiras
• Abstract summary: This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. It offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.
• Score: 7.704032792820767
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.

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