Abstract
Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bulldog nostrils, including both real and AI-generated samples. Five neural network architectures were tested across three experiments, with DenseNet201 achieving the highest median F-score of 54.04%. The model's performance was directly compared to trained human evaluators specializing in veterinary anatomy, achieving comparable levels of accuracy and reliability. These results demonstrate the potential of advanced neural networks to match human-level performance in diagnosis, paving the way for enhanced treatment planning and overall animal welfare.