Brachycephalic Obstructive Airway Syndrome (BOAS) classification in dogs based on respiratory noise analysis using machine learning
Examensarbete för masterexamen
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.
Brachycephalic Obstructive Airway Syndrome , BOAS , Machine learning , Convolutional Neural Network , CNN , Mel-Frequency Cepstral Coefficients , MFCC , Recurrent Neural Network , Long Short-Term Memory , RNN-LSTM , Respiratory noise analysis