A Novel Melspectrogram Snippet Representation Learning Framework for Severity Detection of Chronic Obstructive Pulmonary Diseases

A chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely, RespiratoryDatabase@TR, for five different severity levels of COPD at the Antakya State Hospital, Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and an extreme learning machine (ELM) classifier, respectively. The classification accuracies (ACC) of 95.84% and 94.31% were achieved for two classes and five classes, respectively. In this article, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: data augmentation and preprocessing, melspectrogram snippet representation generation from lung sound, and fine-tuning of a pretrained YAMNet. An experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves the accuracies of 99.25% and 96.14% for binary and multiclass COPD severity classification, respectively, which are superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds.