Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması

Year 2025, Volume: 15 Issue: 4, 1668 - 1695, 15.12.2025

Hayati Türe , Eren Aygün

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

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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