# X-ray Scattering Image Classification Using Deep Learning

arXiv: https://arxiv.org/abs/1611.03313 · HTML: https://mlsanity.com/paper/1611.03313/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [DLSIA: Deep Learning for Scientific Image Analysis](https://mlsanity.com/paper/2308.02559/) (2023)
- [Machine Learning-Assisted Analysis of Small Angle X-ray Scattering](https://mlsanity.com/paper/2111.08645/) (2021)
- [Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data](https://mlsanity.com/paper/2202.10983/) (2022)

## Abstract (arXiv)

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.