  The generation of protein crystals is necessary for the study of protein
molecular function and structure. This is done empirically by processing large
numbers of crystallization trials and inspecting them regularly in search of
those with forming crystals. To avoid missing the hard-gained crystals, this
visual inspection of the trial X-ray images is done manually as opposed to the
existing less accurate machine learning methods. To achieve higher accuracy for
automation, we applied some of the most successful convolutional neural
networks (ResNet, Inception, VGG, and AlexNet) for 10-way classification of the
X-ray images. We showed that substantial classification accuracy is gained by
using such networks compared to two simpler ones previously proposed for this
purpose. The best accuracy was obtained from ResNet (81.43%), which corresponds
to a missed crystal rate of 5.9%. This rate could be lowered to less than 0.1%
by using a top-3 classification strategy. Our dataset consisted of 486,000
internally annotated images, which was augmented to more than a million to
address class imbalance. We also provide a label-wise analysis of the results,
identifying the main sources of error and inaccuracy.
