Automated Lung Sound-Based Disease Classification Using a Multi-Model Pre-Trained CNN Framework

Nisha Palanisamy; Vijayakumar Jeganathan

doi:10.46604/emsi.2026.16374

Authors

Nisha Palanisamy Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India
Vijayakumar Jeganathan Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India

DOI:

https://doi.org/10.46604/emsi.2026.16374

Keywords:

lung sound classification, deep learning, convolutional neural networks, pre-trained models, computer-aided diagnosis

Abstract

The early identification of pulmonary diseases is essential for timely treatment and better patient care. This study presents a deep learning framework for automated classification of lung sound based diseases using nine pre-trained convolutional neural networks (CNNs). The lung sounds are filtered using a sixth-order Butterworth bandpass filter and converted into log-Mel spectrograms before training. Evaluations of the candidate models include to ensure the reliability and clinical relevance of the diagnostic results. EfficientNet-B0 achieves the highest accuracy of 99.20%, precision of 99.20%, sensitivity of 99.20%, specificity of 99.73%, and F1-score of 99.22%, followed by DenseNet201 (99.00%) and ResNet101 (98.90%). These results demonstrate that pre-trained CNNs can learn effective, discriminative features from lung sounds, leading to precise and efficient classification of respiratory diseases.

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