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

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.

Overview of the proposed methodology for pulmonary disease classification using CQT-VMD and pre-trained AlexNet architecture applied to lung sound signals