An Effective Galaxy Classification Using Fractal Analysis and Neural Network

Astronomy is always in a quest of revealing the mysteries of our Universe. There is a vast amount of astronomical data collected and this information comes from stars, galaxies and other celestial objects. While exploring this type of astronomical data, we can identify some complex selfsimilar
patterns. Such self-similar patterns are shown in our own galaxy and are called fractals. This research work has been developed for finding such self-similarity that can be measured from galaxy clusters and this feature can be learned through a suitable neural network. This research work gives an insight about calculating the fractal dimension of galaxy images using a box counting algorithm and training the images using LeNet - 5. The box counting fractal dimension is a specified range of values for each particular class of galaxy. By using the fractal dimension as a primary feature of different classes of galaxy and with the help of LeNet-5 network model classifying the galaxy images into ten specified classes according to its morphological properties. The model produced an accuracy of 74% when implemented with the baseline algorithm. When implemented with LeNet- 5 it produced an accuracy of 96%. The precision recall and f1-Score value of the LeNet-5 model was also calculated. The precision recall and f1-Score value for class 1, class 2, class 4 and class 6 were higher than those of the other classes.