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All Journal Bulletin of Electrical Engineering and Informatics
Hana, Elvina Nur
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Electrical & Electronics Engineering

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Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Hana, Elvina Nur; Faisal, Mohammad Reza; Kartini, Dwi; Mazdadi, Muhammad Itqan; Saputro, Setyo Wahyu; Indriani, Fatma; Satou, Kenji
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Co-Authors Dwi Kartini, Dwi Fatma Indriani Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Saputro, Setyo Wahyu Satou, Kenji