  In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey
(SDSS) have resulted in generation of tremendous amount of data. The
classification of this enormous amount of data by astronomers is time
consuming. To simplify this process, in 2007 a volunteer-based citizen science
project called Galaxy Zoo was introduced, which has reduced the time for
classification by a good extent. However, in this modern era of deep learning,
automating this classification task is highly beneficial as it reduces the time
for classification. For the last few years, many algorithms have been proposed
which happen to do a phenomenal job in classifying galaxies into multiple
classes. But all these algorithms tend to classify galaxies into less than six
classes. However, after considering the minute information which we know about
galaxies, it is necessary to classify galaxies into more than eight classes. In
this study, a neural network model is proposed so as to classify SDSS data into
10 classes from an extended Hubble Tuning Fork. Great care is given to disc
edge and disc face galaxies, distinguishing between a variety of substructures
and minute features which are associated with each class. The proposed model
consists of convolution layers to extract features making this method fully
automatic. The achieved test accuracy is 84.73 per cent which happens to be
promising after considering such minute details in classes. Along with
convolution layers, the proposed model has three more layers responsible for
classification, which makes the algorithm consume less time.
