Deep Learning-Based Lung Sound Classification Using Mel-Spectrogram Features for Early Detection of Respiratory Diseases

Midfai Yabani; Mohammad Reza Faisal; Fatma Indriani; Dodon Turianto Nugrahadi; Dwi Kartini; Kenji Satou

doi:10.35882/jeeemi.v8i1.1256

Midfai Yabani Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0009-0004-0321-8352
Mohammad Reza Faisal Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Fatma Indriani Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0009-0006-7180-6708
Dodon Turianto Nugrahadi Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-7746-2658
Dwi Kartini Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0002-7382-5084
Kenji Satou Faculty of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan https://orcid.org/0000-0001-5474-7087

DOI: https://doi.org/10.35882/jeeemi.v8i1.1256

Keywords: Lung Sound, Feature Extraction, Mel-Spectrogram, Audio Classification, Convolutional Neural Network

Abstract

Respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia remain among the leading causes of death globally. Traditional diagnostic approaches, including auscultation, rely heavily on the subjective expertise of medical practitioners and the quality of the instruments used. Recent advancements in artificial intelligence offer promising alternatives for automated lung sound analysis. However, audio is an unstructured data format that must be converted into a suitable format for AI algorithms. Another significant challenge lies in the imbalanced class distribution within available datasets, which can adversely affect classification performance and model reliability. This study applied several comprehensive preprocessing techniques, including random undersampling to address data imbalance, resampling audio at 4000 Hz for standardization, and standardizing audio duration to 2.7 seconds for consistency. Feature extraction was then performed using the Mel Spectrogram method, converting audio signals into image representations to serve as input for classification algorithms based on deep learning architectures. To determine optimal performance characteristics, various Convolutional Neural Network (CNN) architectures were systematically evaluated, including LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-50, and ResNet-152. VGG-16 achieved the highest classification accuracy of the tested models at 75.5%, demonstrating superior performance in respiratory sound classification tasks. This study demonstrates the potential of AI-based lung sound classification systems as a complementary diagnostic tool for healthcare professionals and the general public in supporting early identification of respiratory abnormalities and diseases. The findings suggest that automated lung sound analysis could enhance diagnostic accessibility and provide more valuable support for clinical decision-making in respiratory healthcare applications

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