  The veterinary field lacks automation in contrast to the tremendous
technological advances made in the human medical field. Implementation of
machine learning technology can shorten any step of the automation process.
This paper explores these core concepts and starts with automation in sorting
radiographs for canines by view and anatomy. This is achieved by developing a
new lightweight algorithm inspired by AlexNet, Inception, and SqueezeNet. The
proposed module proves to be lighter than SqueezeNet while maintaining accuracy
higher than that of AlexNet, ResNet, DenseNet, and SqueezeNet.
