  We present the construction of an image similarity retrieval engine for the
morphological classification of galaxies using the Convolutional AutoEncoder
(CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey
galaxy images listed in the Galaxy Zoo 2 (GZ2) catalog. The visually similar
output images returned by the trained CAE suggest that the encoder efficiently
compresses input images into latent features, which are then used to calculate
similarity parameters. Our Tool for Searching a similar Galaxy Image based on a
Convolutional Autoencoder using Similarity (TSGICAS) leverages this similarity
parameter to classify galaxies' morphological types, enabling the
identification of a wider range of classes with high accuracy compared to
traditional supervised ML techniques. This approach streamlines the
researcher's work by allowing quick prioritization of the most relevant images
from the latent feature database. We investigate the accuracy of our automatic
morphological classifications using three galaxy catalogs: GZ2, Extraction de
Formes Id\'ealis\'ees de Galaxies en Imagerie (EFIGI), and Nair $\&$ Abraham
(NA10). The correlation coefficients between the morphological types of input
and retrieved galaxy images were found to be 0.735, 0.811, and 0.815 for GZ2,
EFIGI, and NA10 catalogs, respectively. Despite differences in morphology tags
between input and retrieved galaxy images, visual inspection showed that the
two galaxies were very similar, highlighting TSGICAS's superior performance in
image similarity search. We propose that morphological classifications of
galaxies using TSGICAS are fast and efficient, making it a valuable tool for
detailed galaxy morphological classifications in other imaging surveys.
