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

TL;DR

This paper introduces a deep convolutional neural network for galaxy classification into elliptical, spiral, and irregular types, achieving high accuracy and outperforming previous methods.

Contribution

It presents a novel 8-layer CNN architecture specifically designed for galaxy classification with superior accuracy over existing approaches.

Findings

Achieved 97.27% testing accuracy.

Outperformed related works in galaxy classification.

Effective deep learning model for astronomical image analysis.

Abstract

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.