# X-ray Scattering Image Classification Using Deep Learning

Source: [X-ray Scattering Image Classification Using Deep Learning](<https://www.sota2.com/research/paper/68fa62d61e209131ce9fb7e0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:28:18.594Z

Authors: Boyu Wang, Kevin Yager, Dantong Yu, Minh Hoai

Published: 2016-11-10T14:32:24.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1611.03313>)

[PDF](<https://arxiv.org/pdf/1611.03313.pdf>)