Deep Galaxy V2: Robust Deep Convolutional Neural Networks for Galaxy Morphology Classifications

Citation:
Khalifa, N. E., M. H. Taha, A. E. Hassanien, and I. Selim, "Deep Galaxy V2: Robust Deep Convolutional Neural Networks for Galaxy Morphology Classifications", The IEEE International Conference on Computing Sciences and Engineering (ICCSE), Kuwait City, Kuwait, pp. 122-127, 2018. copy at www.tinyurl.com/y5cmjrnt

Abstract:

This paper is an extended version of "Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks". In this paper, a robust deep convolutional neural network architecture for galaxy morphology classification is presented. A galaxy can be classified based on its features into one of three categories (Elliptical, Spiral, or Irregular) according to the Hubble galaxy morphology classification from 1926. The proposed convolutional neural network architecture consists of 8 layers, including one main convolutional layer for feature ex-traction with 96 filters and two principle fully connected layers for classification. The architecture is trained over 4238 images and achieved a 97.772% testing accuracy. In this version, "Deep Galaxy V2", an augmentation process is applied to the training data to overcome the overfitting problem and make the proposed architecture more robust and immune to memorizing the training data. A comparative result is present, and the testing accuracy was compared with those of other related works. The proposed architecture outperformed the other related works in terms of its testing accuracy.

Related External Link

DOI:

10.1109/ICCSE1.2018.8374210

Date of Conference:

11-13 March 2018

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