Classification of Protein Crystallization Images using EfficientNet with Data Augmentation

Abstract

In this paper, we applied EfficientNet, a scalable deep convolution neural network, with a custom data augmentation stage to a public protein crystallization image dataset called MARCO. The MARCO dataset has 493,214 protein crystallization images collected from several well-known institutions. In our experiments, EfficientNet outperformed the accuracies reported in the previous studies, and it reached an overall 96.71% testing and 91.33% validation accuracy on the dataset. Also, EfficientNet achieved 97.23% crystal detection accuracy in testing data, which is significant improvement over existing studies.