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Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1042)


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.


  • Convolution neural network (CNN)
  • Softmax
  • Dropout
  • Galaxy type

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  • DOI: 10.1007/978-981-32-9949-8_40
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Correspondence to Diganta Misra .

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Misra, D., Mohanty, S.N., Agarwal, M., Gupta, S.K. (2020). Convoluted Cosmos: Classifying Galaxy Images Using Deep Learning. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore.

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