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Implementation of Siamese-Based Few-Shot Learning Algorithms for the Distinction of COPD and Asthma Subjects

  • This paper investigates the practicality of applying braininspired Few-Shot Learning (FSL) algorithms for addressing shortcomings of Machine Learning (ML) methods in medicine with limited data availability. As a proof of concept, the application of ML for the detection of Chronic Obstructive Pulmonary Disease (COPD) patients was investigated. The complexities associated with the distinction of COPD and asthma patients and the lack of sufficient training data for asthma subjects impair the performance of conventional ML models for the recognition of COPD. Therefore, the objective of this study was to implement FSL methods for the distinction of COPD and asthma subjects with a few available data points. The proposed FSL models in this work were capable of recognizing asthma and COPD patients with 100% accuracy, demonstrating the feasibility of the approach for applications such as medicine with insufficient data availability.

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Metadaten
Author: Pouya Soltani Zarrin, Christian WengerORCiD
DOI:https://doi.org/10.1007/978-3-030-61609-0_34
Title of the source (English):Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I
Publisher:Springer
Place of publication:Cham
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2020
Tag:COPD; Machine Learning
First Page:431
Last Page:440
Series ; volume number:Lecture Notes in Computer Science book series ; volume 12396
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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