Title: Acoustic analysis of chronic obstructive pulmonary disorder using transfer learning: a three-class problem

Authors: John Amose; P. Manimegalai; M. Amritha; S. Thomas George

Addresses: Department of Artificial Intelligence and Data Science, Sri Krishna College of Engineering and Technology, Coimbatore, India ' Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Biomedical Engineering, Kongunadu College of Engineering and Technology, Trichy, India ' Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

Abstract: This study involves a comparative analysis of deep learning and transfer learning techniques for diagnosing the respiratory disease chronic obstructive pulmonary disease (COPD). Unlike many previous machine learning approaches to this problem, we consider it as a three-class problem. Clinicians are required to inform the patient whether they have COPD, or are healthy, or have an unknown respiratory disorder, making this a three-class classification challenge. We use the ICBHI 2017 respiratory challenge dataset for training and evaluation, with the 'unhealthy' class encompassing data from all other respiratory diseases. To preserve time-series information, we transform lung sounds into Mel spectrograms. We train and test both a convolutional neural network (CNN) and a VGG16 model, and the VGG16 model outperforms the CNN with an impressive 95% accuracy.

Keywords: lung sound; convolutional neural network; CNN; Mel spectrogram; VGG16; chronic obstructive pulmonary disease; COPD; transfer learning; pulmonary sounds; precision; recall; F1-score; accuracy.

DOI: 10.1504/IJICA.2025.148627

International Journal of Innovative Computing and Applications, 2025 Vol.15 No.3, pp.135 - 144

Received: 17 Oct 2023
Accepted: 25 Feb 2025
Published online: 16 Sep 2025 *

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