  Automated methods for classifying extragalactic objects in large surveys
offer significant advantages compared to manual approaches in terms of
efficiency and consistency. However, the existence of the Galactic disk raises
additional concerns. These regions are known for high levels of interstellar
extinction, star crowding, and limited data sets and studies. In this study, we
explore the identification and classification of galaxies in the zone of
avoidance (ZoA). In particular, we compare our results in the near-infrared
(NIR) with X-ray data. We analyzed the appearance of objects in the Galactic
disk classified as galaxies using a published machine-learning (ML) algorithm
and make a comparison with the visually confirmed galaxies from the VVV NIRGC
catalog. Our analysis, which includes the visual inspection of all sources
cataloged as galaxies throughout the Galactic disk using ML techniques reveals
significant differences. Only four galaxies were found in both the NIR and
X-ray data sets. Several specific regions of interest within the ZoA exhibit a
high probability of being galaxies in X-ray data but closely resemble extended
Galactic objects. Our results indicate the difficulty in using ML methods for
galaxy classification in the ZoA, which is mainly due to the scarcity of
information on galaxies behind the Galactic plane in the training set. They
also highlight the importance of considering specific factors that are present
to improve the reliability and accuracy of future studies in this challenging
region.
