Galaxy Morphology Classification with Deep Convolutional Neural Networks
  We propose a variant of residual networks (ResNets) for galaxy morphology
classification. The variant, together with other popular convolutional neural
networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo
2 dataset, to classify galaxies into five classes, i.e. completely round
smooth, in-between smooth (between completely round and cigar-shaped),
cigar-shaped smooth, edge-on and spiral. A variety of metrics, such as
accuracy, precision, recall, F1 value and AUC, show that the proposed network
achieves the state-of-the-art classification performance among the networks,
namely, Dieleman, AlexNet, VGG, Inception and ResNets. The overall
classification accuracy of our network on the testing set is 95.2083% and the
accuracy of each type is given as: completely round, 96.6785%; in-between,
94.4238%; cigar-shaped, 58.6207%; edge-on, 94.3590% and spiral, 97.6953%
respectively. Our model algorithm can be applied to large-scale galaxy
classification in forthcoming surveys such as the Large Synoptic Survey
Telescope (LSST).
