Galaxy Type Classification With Sub-categorization Using Deep Learning Framework

This paper presents a robust approach to galaxy categorization utilizing machine learning and image processing techniques, emphasizing Galaxy Zoo and Galaxy10 datasets. The MobileNetV2 architecture is used for feature extraction on a training dataset of 4100 labeled photos. At the same time, image pre-processing procedures ensure dataset standardization. After 20 epochs, the model reaches an overall accuracy of 91%, indicating its usefulness in categorizing galaxies into Elliptical, Spiral, and Irregular types, as well as detailed subclassifications such as E0, E3, E7 for elliptical galaxies and Normal Spiral, Barred Spiral for spiral galaxies. The subclassification models use image processing to extract morphological traits, demonstrating the possibility for automated study of astronomical information and contributing to advances in astrophysics research.