Diagnosing Brachycephalic Obstructive Airway Syndrome in Dogs Using Computer Vision and Machine Learning
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Brachycephalic Obstructive Airway Syndrome (BOAS) is a breathing disorder that
is common among dogs of certain breeds, impairing their quality of life. Diagnosing
BOAS requires licensed veterinarians to undergo specialized training, which limits
accessibility. There is demand for a more accessible solution, which machine learning
could provide. This thesis evaluates two different approaches of machine learning to
explore the possibility of classifying BOAS based on audio recorded from Android
mobile phones, using Python. The first approach extracts signal features from the
audio, which are used to train a simple machine learning model. The second approach
relies on computer vision, using spectrograms; visual representations of the
audio signals, to train a Convolutional Neural Network (CNN). Due to limited data,
I employed augmentation techniques to artificially expand the dataset for training
the models. My findings suggest that the spectrogram-based model is better suited
for the problem, with a perfect prediction accuracy when the dogs were recorded
after a short exercise test, suggesting strong performance under that condition. For
dogs at rest, this model achieved an 81.7% accuracy, indicating somewhat promising
results even under less favourable conditions. However, due to the limited dataset,
the predictive performance was evaluated on few samples, and therefore additional
data is needed for a more robust conclusion. Furthermore, one particular augmentation
technique designed to account for differences in the recording devices’ frequency
response enhanced the model’s general performance and could be further refined to
improve accuracy, especially for dogs at rest.
Beskrivning
Ämne/nyckelord
BOAS, machine learning, computer vision, audio, data augmentation, CNN