  Morphological features in galaxies, like spiral arms, bars, rings, tidal
tails etc. carry information about their structure, origin and evolution. It is
therefore important to catalogue and study such features and to correlate them
with other basic galaxy properties the environment in which the galaxies are
located and their interactions with other galaxies. Surveys such as SDSS,
Pan-STARRS, HSC-SSP have made available very large samples of galaxies for
gainful morphological studies. The availability of galaxy images and catalogues
will increase manifold with future surveys like LSST. The volume of present and
future data is so large that traditional methods, which involve expert
astronomers identifying morphological features through visual inspection, are
no longer sufficient. It is therefore necessary to use AI based techniques like
machine learning and deep learning for finding morphological structures quickly
and efficiently. We report in this study the application of deep learning for
finding ring like structures in galaxy images from the Sloan Digital Sky Survey
(SDSS) data release DR18. We use a catalogue by Buta (2017) of ringed galaxies
from the SDSS to train the network reaching good accuracy and recall, and
generate a catalogue of 29420 galaxies of which 9805 have ring like structures
with prediction confidence exceeding 90 percent. Using a catalogue of barred
galaxy images identified by Abraham et. al. (2018) using deep learning
techniques, we identify a set of 2087 galaxies with bars as well as rings. The
catalogues should be very useful in understanding the origin of these important
morphological structures.
