  In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge
transport barrier. These events are known as edge localized modes (ELMs). ELMs
cause a loss of energy and damage the vessel walls. Understanding the physics
of ELMs and by extension, how to detect and mitigate them, is an important
challenge. In this paper, we focus on two diagnostic methods $\unicode{x2013}$
D-alpha spectroscopy and Doppler backscattering (DBS). The former detects ELMs
by measuring Balmer alpha emission while the latter uses microwave radiation to
probe the plasma. DBS has the advantage of having higher temporal resolution
and robustness to damage. These advantages of DBS diagnostics may be beneficial
for future operational tokamaks and thus data processing techniques for DBS
should be developed in preparation. In sight of this, we explore the training
of neural networks to detect ELMs from DBS data, using D-alpha data as the
ground truth. With shots found in the DIII-D database, the model is trained to
classify each time step based on the occurrence of an ELM event. The results
are promising. When tested on shots similar to those used for training, the
model is capable of consistently achieving a high f1-score of 0.93. This score
is a performance metric for imbalanced datasets that ranges between 0 and 1. We
evaluate the performance of our neural network on a variety of ELMs
$\unicode{x2013}$ grasssy, suppressed, and wide pedestal $\unicode{x2013}$
finding broad applicability. Beyond ELMs, our work demonstrates the wider
feasibility of applying neural networks to data from DBS diagnostics.
