Comparative Analysis of Conditional Deep Convolutional and Wasserstein GAN Architectures for Brain Tumor MRI Data Augmentation

Abstract

Compares conditional deep convolutional and Wasserstein GAN architectures for augmenting brain tumor MRI datasets.

This work intersects with research areas including GAN, deep learning, brain tumor, MRI, data augmentation, DCGAN, Wasserstein GAN, and medical imaging. It is part of the broader research portfolio of Nitiraj V. Kulkarni in the domain of peer-reviewed academic publishing.

Keywords & Topics

GAN, deep learning, brain tumor, MRI, data augmentation, DCGAN, Wasserstein GAN, medical imaging

How to Cite

APA

Nitiraj V. Kulkarni et al. (2026). Comparative Analysis of Conditional Deep Convolutional and Wasserstein GAN Architectures for Brain Tumor MRI Data Augmentation. International Conference on Sustainable Innovation with AI and ML, Pages 223–233, Atlantis Press. Atlantis Press.

IEEE

Nitiraj V. Kulkarni et al., "Comparative Analysis of Conditional Deep Convolutional and Wasserstein GAN Architectures for Brain Tumor MRI Data Augmentation," International Conference on Sustainable Innovation with AI and ML, Pages 223–233, Atlantis Press, 2026.

BibTeX

@article{2026_gan_brain_tumor_mri_augmentation,
  title={Comparative Analysis of Conditional Deep Convolutional and Wasserstein GAN Architectures for Brain Tumor MRI Data Augmentation},
  author={Nitiraj V. Kulkarni et al.},
  journal={International Conference on Sustainable Innovation with AI and ML},
  year={2026},
  publisher={Atlantis Press}
}

Identifiers & Links

Last updated: 2026-01-01

About the Author

Nitiraj V. Kulkarni is an AI safety and cybersecurity researcher based in Pune, India, with 35+ peer-reviewed publications, 5 patents, 6 copyright registrations, and 15,000+ open datasets published on Kaggle and Zenodo. He serves as a peer reviewer for 11+ international journals and conferences. Read full profile.