Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması

Hayati Türe; Eren Aygün

doi:10.31466/kfbd.1715285

TR EN

Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması

Abstract

Bu çalışma, ICBHI 2017 solunum sesi veri seti üzerinde geliştirilmiş bir derin öğrenme yaklaşımıyla, normal ve patolojik solunum seslerini otomatik olarak sınıflandırmayı amaçlamaktadır. Çalışmada, segmentlenmiş solunum seslerinden MFCC, Mel spektrogram ve spektral öznitelikler elde edilmiş; ardından bu öznitelikler klasik makine öğrenmesi algoritmaları (XGBoost, SVM, KNN, Rastgele Orman) ve derin öğrenme modelleri (GhostNet v1-v4, EfficientNet-B0, ResNet50, MobileNetV3) ile eğitilmiştir. Veri artırma tekniklerinin (augmentasyon) katkısı da sistematik olarak incelenmiştir. Sonuçlar, GhostNet v4 modelinin %89 doğruluk ve 0.89 F1-skoru ile en iyi performansı gösterdiğini ortaya koymaktadır. Bu doğruluk oranı, ICBHI 2017 veri seti ile literatürde rapor edilen birçok yöntemi geride bırakmaktadır. Ayrıca, karışıklık matrisi analizleri modelin normal ve patolojik sınıfları yüksek tutarlılıkla ayırt edebildiğini göstermektedir. Elde edilen sonuçlar, akciğer seslerinin otomatik analizi için derin öğrenme temelli modellerin etkinliğini ortaya koymakta ve klinik karar destek sistemlerine entegre edilebilecek potansiyel çözümler sunmaktadır.

Keywords

Solunum sesi analizi, Derin öğrenme, Makine Öğrenmesi, MFCC, Mel spektrogram, GhostNet

Comparison of Classical and Deep Learning Models for Respiratory Sound Classification

Abstract

This study aims to automatically classify normal and pathological respiratory sounds using a deep learning-based approach developed on the ICBHI 2017 respiratory sound dataset. MFCCs, Mel spectrograms, and spectral features were extracted from segmented respiratory sound recordings. These features were then used to train classical machine learning algorithms (XGBoost, SVM, KNN, Random Forest) and deep learning models (GhostNet v1–v4, EfficientNet-B0, ResNet50, MobileNetV3). The impact of data augmentation techniques was also systematically examined. The results demonstrate that the GhostNet v4 model achieved the highest performance with 89% accuracy and an F1-score of 0.89. This accuracy outperforms many existing methods reported in the literature using the same dataset. Confusion matrix analyses further indicate that the model reliably distinguishes between normal and pathological classes. These findings highlight the effectiveness of deep learning-based models in the automatic analysis of respiratory sounds and suggest promising solutions for integration into clinical decision support systems.

Keywords

Respiratory sound analysis, Deep learning, Machine learning, MFCC, Mel spectrogram, GhostNet

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Details

Primary Language

Turkish

Subjects

Computer Software

Journal Section

Research Article

Authors

Hayati Türe
0000-0003-3012-8016
Türkiye

Eren Aygün ^*
0009-0008-0166-5596
Türkiye

Publication Date

December 15, 2025

Submission Date

June 5, 2025

Acceptance Date

July 29, 2025

Published in Issue

Year 2025 Volume: 15 Number: 4

DOI

https://doi.org/10.31466/kfbd.1715285

IZ

https://izlik.org/JA83HT59MJ

APA

Türe, H., & Aygün, E. (2025). Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. Karadeniz Fen Bilimleri Dergisi, 15(4), 1668-1695. https://doi.org/10.31466/kfbd.1715285

AMA

1.Türe H, Aygün E. Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. KFBD. 2025;15(4):1668-1695. doi:10.31466/kfbd.1715285

Chicago

Türe, Hayati, and Eren Aygün. 2025. “Solunum Sesi Sınıflandırması Için Klasik Ve Derin Öğrenme Modellerinin Karşılaştırılması”. Karadeniz Fen Bilimleri Dergisi 15 (4): 1668-95. https://doi.org/10.31466/kfbd.1715285.

EndNote

Türe H, Aygün E (December 1, 2025) Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. Karadeniz Fen Bilimleri Dergisi 15 4 1668–1695.

IEEE

[1]H. Türe and E. Aygün, “Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması”, KFBD, vol. 15, no. 4, pp. 1668–1695, Dec. 2025, doi: 10.31466/kfbd.1715285.

ISNAD

Türe, Hayati - Aygün, Eren. “Solunum Sesi Sınıflandırması Için Klasik Ve Derin Öğrenme Modellerinin Karşılaştırılması”. Karadeniz Fen Bilimleri Dergisi 15/4 (December 1, 2025): 1668-1695. https://doi.org/10.31466/kfbd.1715285.

JAMA

1.Türe H, Aygün E. Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. KFBD. 2025;15:1668–1695.

MLA

Türe, Hayati, and Eren Aygün. “Solunum Sesi Sınıflandırması Için Klasik Ve Derin Öğrenme Modellerinin Karşılaştırılması”. Karadeniz Fen Bilimleri Dergisi, vol. 15, no. 4, Dec. 2025, pp. 1668-95, doi:10.31466/kfbd.1715285.

Vancouver

1.Hayati Türe, Eren Aygün. Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. KFBD. 2025 Dec. 1;15(4):1668-95. doi:10.31466/kfbd.1715285

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Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması

Abstract

Keywords

Comparison of Classical and Deep Learning Models for Respiratory Sound Classification

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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