VGG16-Based Deep Learning Architectures for Classification of Lung Sounds into Normal, Crackles, and Wheezes using Gammatonegrams

Breathing sounds are a rich source of information that can assist doctors in diagnosing pulmonary diseases in a non-invasive manner. Several algorithms can be developed based on these sounds to create an automatic classification system for lung diseases. To implement these systems, researchers traditionally follow two main steps: feature extraction and pattern classification. In recent years, deep neural networks have gained attention in the field of breathing sound classification as they have proven effective for training large datasets. In this study, we conducted a comparison of two versions of the VGG16-based deep learning model for breathing sound classification using Gammatonegrams as input. We implemented two extensions of the VGG16 model - one executed from scratch and the other based on a pretrained VGG16 model using transfer learning. We processed digital recordings of cycle-based breathing sounds to obtain Gammatonegrams images, which were then fed as input to the VGG16 network. In addition, we performed data augmentation in our experiments using audio cycles from the ICBHI database to evaluate the performance of the proposed method. The classification results were obtained using the Google Collaboratory platform.