X-ray Scattering Image Classification Using Deep Learning

TL;DR

This paper demonstrates that deep learning, specifically CNNs and autoencoders, can effectively classify x-ray scattering images, outperforming previous methods by 10% on synthetic and real datasets.

Contribution

It introduces the use of deep learning models for x-ray scattering image classification and leverages synthetic data for training.

Findings

Deep learning methods outperform previous approaches by 10%.

Synthetic data effectively trains deep learning models.

CNNs and autoencoders are suitable for this classification task.

Abstract

Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images. In particular, we apply Convolutional Neural Networks and Convolutional Autoencoders for x-ray scattering image classification. To acquire enough training data for deep learning, we use simulation software to generate synthetic x-ray scattering images. Experiments show that deep learning methods outperform previously published methods by 10\% on synthetic and real datasets.