  Purpose: To assess the capability of deep convolutional neural networks to
classify anatomical location and projection from a series of 48 standard views
of racehorse limbs.
  Materials and Methods: 9504 equine pre-import radiographs were used to train,
validate, and test six deep learning architectures available as part of the
open source machine learning framework PyTorch.
  Results: ResNet-34 achieved a top-1 accuracy of 0.8408 and the majority (88%)
of misclassification was because of wrong laterality. Class activation maps
indicated that joint morphology drove the model decision.
  Conclusion: Deep convolutional neural networks are capable of classifying
equine pre-import radiographs into the 48 standard views including moderate
discrimination of laterality independent of side marker presence.
