Brachycephalic Obstructive Airway Syndrome (BOAS) classification in dogs based on respiratory noise analysis using machine learning
| dc.contributor.author | Mårtensson, Moa | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.examiner | Karlsteen, Magnus | |
| dc.contributor.supervisor | Karlsteen, Magnus | |
| dc.contributor.supervisor | Skiöldebrand, Eva | |
| dc.date.accessioned | 2021-02-26T12:32:11Z | |
| dc.date.available | 2021-02-26T12:32:11Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Brachycephalic Obstructive Airway Syndrome (BOAS) is a problem in several dog breeds due to a compressed shape of the skull. It is classified as BOAS grade 0-3, where 0 is normal breathing and 3 is the most severe grade of the syndrome. Grade 2-3 can cause great suffering for the affected dogs and needs treatment. This study aimed to find a method using machine learning to classify the BOAS grade based on audio recordings of respiratory noise. The recordings were converted into Mel-Frequency Cepstral Coefficients (MFCCs) to be processed as images by the network. The results proved that Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) was a successful method to classify the four different BOAS grades with an accuracy of about 86-87% for dictaphone recordings and about 62-66% for stethoscope recordings. Convolutional Neural Networks (CNN) also managed to classify the BOAS grades but this method was less accurate, with an accuracy of approximately 74-76% for dictaphone recordings and 50-54% for stethoscope recordings. The study was a collaboration between Chalmers University of Technology and Swedish University of Agricultural Sciences. | sv |
| dc.identifier.coursecode | TIFX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/302233 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Brachycephalic Obstructive Airway Syndrome | sv |
| dc.subject | BOAS | sv |
| dc.subject | Machine learning | sv |
| dc.subject | Convolutional Neural Network | sv |
| dc.subject | CNN | sv |
| dc.subject | Mel-Frequency Cepstral Coefficients | sv |
| dc.subject | MFCC | sv |
| dc.subject | Recurrent Neural Network | sv |
| dc.subject | Long Short-Term Memory | sv |
| dc.subject | RNN-LSTM | sv |
| dc.subject | Respiratory noise analysis | sv |
| dc.title | Brachycephalic Obstructive Airway Syndrome (BOAS) classification in dogs based on respiratory noise analysis using machine learning | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Biomedical engineering (MPBME), MSc |