Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
subject: CNN; Galaxy; Parallel CNN; ResNet; Star; VGG16
description: This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE.
creator: Premanand N.; Tarun V.G.; Pawar S.; Jawakar D.N.; Utsav; Deepa S.; Jayapriya J.; Vinay M.
source: 2023 International Conference on Data Science and Network Security, ICDSNS 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030159-5
format: Online
language: English
type: Conference paper
coverage: Premanand N., Christ University, Department of Computer Science, Bengaluru, India; Tarun V.G., Christ University, Department of Computer Science, Bengaluru, India; Pawar S., Christ University, Department of Computer Science, Bengaluru, India; Jawakar D.N., Christ University, Department of Computer Science, Bengaluru, India; Utsav, Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India