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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2023
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28048/1/VGG16-based%20deep%20learning%20architectures%20for%20classification%20of%20lung%20sounds%20into%20normal%2C%20crackles%2C%20and%20wheezes%20using%20gammatonegrams.pdf http://eprints.utem.edu.my/id/eprint/28048/ https://www.researchgate.net/publication/377242904_Advancing_Educational_Practices_Implementation_and_Impact_of_Virtual_Reality_in_Islamic_Religious_Education |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|