1 research outputs found
Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks
In this paper, a deep convolutional neural network architecture for galaxies
classification is presented. The galaxy can be classified based on its features
into main three categories Elliptical, Spiral, and Irregular. The proposed deep
galaxies architecture consists of 8 layers, one main convolutional layer for
features extraction with 96 filters, followed by two principles fully connected
layers for classification. It is trained over 1356 images and achieved 97.272%
in testing accuracy. A comparative result is made and the testing accuracy was
compared with other related works. The proposed architecture outperformed other
related works in terms of testing accuracy.Comment: 4 pages, 6 figures, 2 tables, Conferenc