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2020 | OriginalPaper | Chapter

Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning

Authors : Diganta Misra, Sachi Nandan Mohanty, Mohit Agarwal, Suneet K. Gupta

Published in: Data Management, Analytics and Innovation

Publisher: Springer Singapore

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Abstract

In this paper, a deep learning-based approach has been developed to classify the images of galaxies into three major categories, namely, elliptical, spiral, and irregular. The classifier successfully classified the images with an accuracy of 97.3958%, which outperformed conventional classifiers like Support Vector Machine and Naive Bayes. The convolutional neural network architecture involves one input convolution layer having 16 filters, followed by 4 hidden layers, 1 penultimate dense layer, and an output Softmax layer. The model was trained on 4614 images for 200 epochs using NVIDIA-DGX-1 Tesla-V100 Supercomputer machine and was subsequently tested on new images to evaluate its robustness and accuracy.

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Metadata
Title
Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning
Authors
Diganta Misra
Sachi Nandan Mohanty
Mohit Agarwal
Suneet K. Gupta
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9949-8_40