This paper is in the following e-collection/theme issue:

Foundations of AI (49) Artificial Intelligence (1602) Clinical Information and Decision Making (1547) New Methods (1003) Machine Learning (1627) Tools, Programs and Algorithms (305) mHealth for Data Collection and Research (1034) Respiratory Medicine (106) Spatial Audio in XR and Spatial Computing (1)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67239, first published .
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Authors of this article:

Jing-Tong Tzeng1 Author Orcid Image ;   Jeng-Lin Li2 Author Orcid Image ;   Huan-Yu Chen2 Author Orcid Image ;   Chu-Hsiang Huang3 Author Orcid Image ;   Chi-Hsin Chen3 Author Orcid Image ;   Cheng-Yi Fan3 Author Orcid Image ;   Edward Pei-Chuan Huang3 Author Orcid Image ;   Chi-Chun Lee2 Author Orcid Image
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