The ongoing and forthcoming surveys will result in an unprecedented increase in the
number of observed galaxies. As a result, data-driven techniques are now the primary …

We propose a variant of residual networks (ResNets) for galaxy morphology classification.
The variant, together with other popular convolutional neural networks (CNNs), is applied to …

Measuring the morphological parameters of galaxies is a key requirement for studying their
formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the …

Galaxy morphology reflects structural properties that contribute to the understanding of the
formation and evolution of galaxies. Deep convolutional networks have proven to be very …

Classifying the morphologies of galaxies is an important step in understanding their physical
properties and evolutionary histories. The advent of large-scale surveys has hastened the …

The increased volume of images and galaxies surveyed by recent and upcoming projects
consolidates the need for accurate and scalable automated AI-driven classification methods …

Classification of galaxy morphology is a hot issue in astronomical research. Although
significant progress has been made in the last decade in classifying galaxy morphology …

Context. The classification of galaxy morphology is among the most active fields in
astronomical research today. With the development of artificial intelligence technology, deep …

Understanding morphological types of galaxies is a key parameter for studying their
formation and evolution. Neural networks that have been used previously for galaxy …

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 …