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

TL;DR

This paper introduces a deep convolutional neural network with a finder module for recognizing protein crystallization states from X-ray images, achieving high accuracy with limited data.

Contribution

The study proposes a novel CNN approach with a finder module that improves recognition accuracy on small datasets for protein crystallization images.

Findings

Achieved about 5% higher AUC than Inception-V3-based methods.

Effective recognition with fewer training images.

Validated through multiple experiments on crystallization images.

Abstract

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