  Recently, machine learning methods presented a viable solution for automated
classification of image-based data in various research fields and business
applications. Scientists require a fast and reliable solution to be able to
handle the always growing enormous amount of data in astronomy. However, so far
astronomers have been mainly classifying variable star light curves based on
various pre-computed statistics and light curve parameters. In this work we use
an image-based Convolutional Neural Network to classify the different types of
variable stars. We used images of phase-folded light curves from the OGLE-III
survey for training, validating and testing and used OGLE-IV survey as an
independent data set for testing. After the training phase, our neural network
was able to classify the different types between 80 and 99%, and 77-98%
accuracy for OGLE-III and OGLE-IV, respectively.
