Detection of Fractures in Dogs' Long Bones
Abstract: Especially in recent years, deep learning algorithms have been used in several fields, including in health and biomedicine. Deep learning architectures, especially convolutional neural networks are powerful feature extractors for image-based recognition tasks such as fracture detection, tissue and organ division, disease detection, etc. Today, the detection of fractures in human bones has become a popular topic. However, since there are not many studies on this topic in veterinary medicine, there is a gap here in the literature. This study was conducted to contribute to the literature in the field of veterinary medicine. In the study, long bones in dogs are classified as broken or intact. The dataset used is one that’s newly presented to the literature. The dataset consists of X-ray images of long bones of dogs taken from Ankara Metropolitan Municipality Temporary Care Home for Street Animals. In this study, end-to-end and transfer learning methods were used. This study was conducted on the MATLAB platform and the highest success was achieved with the deep learning based Resnet50 network structure at 91.7% when the feature was extracted and classified with SVM.
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