Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.

Breast cancer has a high incidence and mortality rate in the female population. Mammography is the most reliable method for early and accurate diagnosis of breast cancer. Automated detection and classification of breast masses on mammograms is a challenging task and is essential to assist radiologists in accurately diagnosing breast masses. The aim of this study is to develop a Computer-Aided Diagnosis (CAD) system based on You Look Only Once (YOLO) for identifying breast masses and classifying them as benign or malignant. We propose a YOLOv5-CAD framework that uses a transfer learning approach. Two datasets, CBIS-DDSM and VinDr-Mammo, are utilized for training from scratch. The model weights and parameters are subsequently transferred and fine-tuned onto the smaller INBreast dataset. Furthermore, an analysis is conducted to assess the impact of various data augmentation techniques during the training phase on enhancing model performance. The proposed framework demonstrates encouraging fivefold cross-validation evaluation results. To conclude, transfer learning from CBIS-DDSM achieves 0.843 mAP, precision of 0.855, recall of 0.774, while transfer learning from VinDr- Mammo reaches 0.84 mAP, precision of 0.829, recall of 0.787. Furthermore, the performance of the two fine-tuned models was tested on both the MIAS dataset and the private dataset from Başkent University Ankara Hospital. Such promising performance could be useful for the CAD frameworks being developed to support radiologists as a second opinion reader for the detection and classification of breast masses.