There are millions of huge collections of stars, gas, dust and stellar remnants all held together by gravity in our vast universe. These collections, or galaxies, help in deciphering the structure and history of the universe in general. The classification of these galaxies based on morphological parameters is a relevant requirement in understanding their formation and evolution. Manual identification of the categories to which each belongs to can be tiresome, time consuming and error prone. The objective of our work was to automate the process of finding the features that characterize a galaxy using convolutional neural networks, a cardinal concept in the image data space, whilst comparing the accuracy of the classification with and without prior processing of the image dataset. The Galaxy Zoo dataset was used for the same and it was preprocessed by applying median filtering and contrast limited adaptive histogram equalization. The final classification model was a CNN based on the VGG-16 architecture with some modifications. We considered all 37 features as per the decision tree by Willet et. al. and with a multiregression approach, obtained a model with a validation loss of 0.0102 (mean square error) on processed images as the best performing model. The model was then deployed onto a client-side interface using Flask to predict the features of the galaxies in real-time

Keywords : Deep Learning, Image Processing, Convolutional Neural Networks