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In this paper, a deep convolutional neural network architecture for galaxiesclassification is presented. The galaxy can be classified based on its featuresinto main three categories Elliptical, Spiral, and Irregular. The proposed deepgalaxies architecture consists of 8 layers, one main convolutional layer forfeatures extraction with 96 filters, followed by two principles fully connectedlayers for classification. It is trained over 1356 images and achieved 97.272%in testing accuracy. A comparative result is made and the testing accuracy wascompared with other related works. The proposed architecture outperformed otherrelated works in terms of testing accuracy.

Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks