# Morphological Classification of Galaxies Using SpinalNet

arXiv: https://arxiv.org/abs/2305.01873 · HTML: https://mlsanity.com/paper/2305.01873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies](https://mlsanity.com/paper/2102.08414/) (2021)
- [SpinalNet: Deep Neural Network with Gradual Input](https://mlsanity.com/paper/2007.03347/) (2020)
- [Star-galaxy Classification Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1608.04369/) (2016)
- [Combining human and machine learning for morphological analysis of galaxy images](https://mlsanity.com/paper/1409.7935/) (2014)
- [Galaxy Morphology Classification using EfficientNet Architectures](https://mlsanity.com/paper/2008.13611/) (2020)

## Built on by (most cited first, 1 of 1)

- [Classification of Tents in Street Bazaars Using CNN](https://mlsanity.com/paper/2506.17946/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.