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

Authors : Ayşen Özün Türkçetin, Turgay Koç, Sule Cilekar

Pages : 896-910

View : 65 | Download : 50

Publication Date : 2025-09-30

Article Type : Research Paper

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

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