  In this work we explore the possibility of applying machine learning methods
designed for one-dimensional problems to the task of galaxy image
classification. The algorithms used for image classification typically rely on
multiple costly steps, such as the Point Spread Function (PSF) deconvolution
and the training and application of complex Convolutional Neural Networks (CNN)
of thousands or even millions of parameters. In our approach, we extract
features from the galaxy images by analysing the elliptical isophotes in their
light distribution and collect the information in a sequence. The sequences
obtained with this method present definite features allowing a direct
distinction between galaxy types, as opposed to smooth S\'ersic profiles. Then,
we train and classify the sequences with machine learning algorithms, designed
through the platform Modulos AutoML, and study how they optimize the
classification task. As a demonstration of this method, we use the second
public release of the Dark Energy Survey (DES DR2). We show that by applying it
to this sample we are able to successfully distinguish between early-type and
late-type galaxies, for images with signal-to-noise ratio greater then 300.
This yields an accuracy of $86\%$ for the early-type galaxies and $93\%$ for
the late-type galaxies, which is on par with most contemporary automated image
classification approaches. Our novel method allows for galaxy images to be
accurately classified and is faster than other approaches. Data dimensionality
reduction also implies a significant lowering in computational cost. In the
perspective of future data sets obtained with e.g. Euclid and the Vera Rubin
Observatory (VRO), this work represents a path towards using a well-tested and
widely used platform from industry in efficiently tackling galaxy
classification problems at the peta-byte scale.
