Abstract Bone fractures in dogs are common orthopaedic conditions that require accurate diagnosis and rapid intervention. Traditional radiographic interpretation is often time consuming and is subject to variability, emphasizing the need for automated diagnostic tools. This paper represents a deep learning model based on classification of long bone fractures in dogs using medical conventional radiographic images. The proposed model uses a convolutional neural network (CNN), specifically ResNet50 to improve detection and fracture classification. Comparative analysis with other deep learning architectures, including VGG16 and MobileNeTV2, shows the excellent ResNet50 performance. To address the challenge of limited annotated veterinary radiographic datasets, the actual data strategy of augmentation is implemented, which increases the generalization of the model. In addition, the segment model of anything (SAM) is integrated for automated fracture segmentation, allowing precise location and improving diagnostic efficiency. Experimental results show that the ResNet50 achieves high classification performance with an accuracy of 99.76%, accuracy of 99.53%, 100% and F1-score 99.76%, overcoming other competing architecture. These findings emphasize the potential of artificial intelligence in veterinary orthopaedic and offer an efficient, accurate and automated solution for diagnosis and control of long bone fractures in social animals.
Saber et al. (Wed,) studied this question.