Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals

Neili, Zakaria and Sundaraj, Kenneth (2025) Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals. Applied Intelligence, 55 (6). pp. 1-19. ISSN 0924-669X

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Abstract

This study presents a novel algorithm for classifying pulmonary diseases using lung sound signals by integrating Variational Mode Decomposition (VMD) and the Constant-Q Transform (CQT) within a pre-trained AlexNet convolutional neural network. Breathing sounds from the ICBHI and KAUHS databases are analyzed, where three key intrinsic mode functions (IMFs) are extracted using VMD and subsequently converted into CQT-based time-frequency representations. These images are then processed by the AlexNet model, achieving an impressive classification accuracy of 93.30%. This approach not only demonstrates the innovative synergy of CQT-VMD for lung sound analysis but also underscores its potential to enhance computerized decision support systems (CDSS) for pulmonary disease diagnosis. The results, showing high accuracy, a sensitivity of 91.21%, and a specificity of 94.9%, highlight the robustness and effectiveness of the proposed method, paving the way for its clinical adoption and the development of lightweight deep-learning algorithms for portable diagnostic tools.

Item Type:	Article
Uncontrolled Keywords:	Pulmonary disorders classification, Variational mode decomposition, Deep learning architectures, Constant-Q transform
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	27 Oct 2025 04:43
Last Modified:	27 Oct 2025 04:43
URI:	http://eprints.utem.edu.my/id/eprint/29030
Statistic Details:	View Download Statistic

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