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Recently, deep convolutional neural networks have shown good results forimage recognition. In this paper, we use convolutional neural networks with afinder module, which discovers the important region for recognition andextracts that region. We propose applying our method to the recognition ofprotein crystals for X-ray structural analysis. In this analysis, it isnecessary to recognize states of protein crystallization from a large number ofimages. There are several methods that realize protein crystallizationrecognition by using convolutional neural networks. In each method, large-scaledata sets are required to recognize with high accuracy. In our data set, thenumber of images is not good enough for training CNN. The amount of data forCNN is a serious issue in various fields. Our method realizes high accuracyrecognition with few images by discovering the region where the crystallizationdrop exists. We compared our crystallization image recognition method with ahigh precision method using Inception-V3. We demonstrate that our method iseffective for crystallization images using several experiments. Our methodgained the AUC value that is about 5% higher than the compared method.

Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module