COUGH SOUND ANALYSIS WITH DEEP LEARNING: THE IMPACT OF DATA AUGMENTATION ON RESPIRATORY DISEASE CLASSIFICATION

Abstract

Respiratory diseases affect millions globally, necessitating efficient and early diagnostic tools to mitigate complications. This study proposes a robust and systematic approach for classifying asthma, COPD, pneumonia, and healthy conditions using cough sound analysis. Mel-frequency cepstral coefficients (MFCCs) were extracted and used to train both a deep learning model (CNN) and traditional classifiers (Random Forest, SVM) under limited and imbalanced data conditions. A major focus was on evaluating the impact of data augmentation and model choice on classification performance. Initial results showed that traditional models outperformed the CNN due to overfitting. However, with progressive augmentation up to 800 synthetic samples per class and the use of Dice Loss, the CNN model achieved substantial improvements, reaching 84% accuracy and a Macro F1 Score of 69%. These results highlight the critical role of data augmentation and tailored training strategies in enhancing the performance of deep learning models for audio-based biomedical classification tasks.

Keywords

• Cough Sound Analysis
• Lung Diseases
• Deep Learning Models
• Data Augmentation
• Convolution Neural Network
• Cross Validation
• Imbalanced Data

Ethical Statement

Afyonkarahisar Health Sciences University ethics committee approval for the data to be collected within the scope of the project was received with the reference number 2023/470, code 2011-KAEK-2, and the ethics committee reports are presented in the attachment.

Thanks

In this study, the dataset was collected from patients hospitalized in the Department of Chest Diseases, Afyonkarahisar Health Sciences University. This study is a part of Ayşen Özün Türkçetin's doctoral dissertation. We thank Afyonkarahisar Health Sciences University for her help during the ethics committee and dataset stages.

DERİN ÖĞRENME İLE ÖKSÜRÜK SESİ ANALİZİ: VERİ ARTIRIMININ SOLUNUM YOLU HASTALIKLARI SINIFLANDIRMASI ÜZERİNDEKİ ETKİSİ

Abstract

Solunum yolu hastalıkları küresel olarak milyonlarca kişiyi etkileyerek komplikasyonları azaltmak için etkili ve erken tanı araçlarının gerekliliğini ortaya koymaktadır. Bu çalışma, öksürük sesi analizini kullanarak astım, KOAH, zatürre ve sağlıklı durumları sınıflandırmak için sağlam ve sistematik bir yaklaşım önermektedir. Mel-frekans cepstral katsayıları (MFCC'ler) çıkarılarak ve sınırlı olan dengesiz veri koşulları altında hem derin öğrenme modelini (CNN) hem de geleneksel sınıflandırıcıları (Rastgele Orman, SVM) eğitmek için kullanılmıştır. Çalışmanın başlıca odak noktası, veri artırmanın ve model seçiminin sınıflandırma performansı üzerindeki etkisini değerlendirmektir. İlk sonuçlar, aşırı uyum nedeniyle geleneksel modellerin CNN'den daha iyi performans gösterdiğini göstermiştir. Ancak, sınıf başına 800 sentetik örneğe kadar kademeli artırma ve Dice Loss kullanımıyla CNN modeli önemli iyileştirmeler elde ederek %84 doğruluk ve %69'luk bir Makro F1 Puanı elde edildi. Bu sonuçlar, ses tabanlı biyomedikal sınıflandırma görevleri için derin öğrenme modellerinin performansını artırmada veri artırmanın ve özel eğitim stratejilerinin kritik rolünü vurgulamaktadır.

Keywords

• Öksürük Sesi Analizi
• Akciğer Hastalıkları
• Derin Öğrenme Modelleri
• Veri Arttırma
• Evrişimli Sinir Ağı
• Çapraz Doğrulama
• Dengesiz Veri

Ethical Statement

Proje kapsamında toplanacak olan veriler için Afyonkarahisar Sağlık Bilimleri Üniversitesi etik kurul onayı 2011-KAEK-2 kodu 2023/470 sayılı referans no ile alınmış olup, etik kurul raporları ekte sunulmuştur.

Thanks

Bu çalışmada veri seti Afyonkarahisar Sağlık Bilimleri Üniversitesi Göğüs Hastalıkları Anabilim Dalı'nda yatan hastalardan toplanmıştır. Bu çalışma Ayşen Özün Türkçetin'in doktora tezinin bir parçasıdır. Etik komite ve veri seti aşamalarında yardımları için Afyonkarahisar Sağlık Bilimleri Üniversitesi'ne teşekkür ederiz.

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