- Home
- Conference
- ICCAIS-2026
- Article
Enhancing Early Alzheimer’s Disease Detection Using GAN-Based MRI Data Augmentation
Authors
1 2 3 4 5 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 146-151
- Article Type
- Research Article
- Check for updates
-
References
1. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2014, pp. 2672–2680.
2. H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” arXiv preprint arXiv:1807.10225, 2018.
3. C. Bowles, L. Chen, R. Guerrero, P. Bentley, R. Gunn, A. Hammers, D. Dickie, M. Herna´ndez, J. Wardlaw, and D. Rueckert, “GAN augmenta- tion: Augmenting training data using generative adversarial networks,” arXiv preprint arXiv:1810.10863, 2018.
4. H.-I. Suk, S.-W. Lee, and D. Shen, “Deep learning-based feature representation for Alzheimer’s disease and mild cognitive impairment classification,” IEEE Trans. Biomedical Engineering, vol. 62, no. 12, pp. 2941–2949, 2014.
5. A. Payan and G. Montana, “Predicting Alzheimer’s disease: A neu- roimaging study with 3D convolutional neural networks,” in Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 355–362.
6. M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” IEEE Trans. Medical Imaging, vol. 37, no. 7, pp. 1661–1673, 2018.
7. V. Gupta, S. Jain, and S. Mishra, “GAN-based data augmentation for improved Alzheimer’s disease classification using MRI images,” in Proc. Int. Conf. Artificial Intelligence and Data Engineering, 2021, pp. 1–6.
8. M. Stone, Dialogue Systems in Artificial Intelligence. Oxford, U.K.: Oxford Univ. Press, 2019.
9. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Pearson, 2023.
10. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. Bharath, “Generative adversarial networks: An overview,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53–65, 2018.
11. X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Medical Image Analysis, vol. 58, pp. 101552, 2019.
12. L. Cui, S. Biswal, L. M. Glass, G. Lever, J. Sun, and C. Xiao, “CONAN: Complementary pattern augmentation for rare disease detection,” in Proc. AAAI Conf. Artificial Intelligence, vol. 34, no. 4, pp. 614–621, 2020.
2. H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” arXiv preprint arXiv:1807.10225, 2018.
3. C. Bowles, L. Chen, R. Guerrero, P. Bentley, R. Gunn, A. Hammers, D. Dickie, M. Herna´ndez, J. Wardlaw, and D. Rueckert, “GAN augmenta- tion: Augmenting training data using generative adversarial networks,” arXiv preprint arXiv:1810.10863, 2018.
4. H.-I. Suk, S.-W. Lee, and D. Shen, “Deep learning-based feature representation for Alzheimer’s disease and mild cognitive impairment classification,” IEEE Trans. Biomedical Engineering, vol. 62, no. 12, pp. 2941–2949, 2014.
5. A. Payan and G. Montana, “Predicting Alzheimer’s disease: A neu- roimaging study with 3D convolutional neural networks,” in Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 355–362.
6. M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” IEEE Trans. Medical Imaging, vol. 37, no. 7, pp. 1661–1673, 2018.
7. V. Gupta, S. Jain, and S. Mishra, “GAN-based data augmentation for improved Alzheimer’s disease classification using MRI images,” in Proc. Int. Conf. Artificial Intelligence and Data Engineering, 2021, pp. 1–6.
8. M. Stone, Dialogue Systems in Artificial Intelligence. Oxford, U.K.: Oxford Univ. Press, 2019.
9. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Pearson, 2023.
10. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. Bharath, “Generative adversarial networks: An overview,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53–65, 2018.
11. X. Yi, E. Walia, and P. Babyn, “Generative adversarial network in medical imaging: A review,” Medical Image Analysis, vol. 58, pp. 101552, 2019.
12. L. Cui, S. Biswal, L. M. Glass, G. Lever, J. Sun, and C. Xiao, “CONAN: Complementary pattern augmentation for rare disease detection,” in Proc. AAAI Conf. Artificial Intelligence, vol. 34, no. 4, pp. 614–621, 2020.
Related Articles
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.