Skip to main content

Advertisement

SpringerLink
Log in

Alzheimer’s disease classification using 3D conditional progressive GAN- and LDA-based data selection

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Alzheimer’s disease is a kind of neurological disorder that directly impacts the memory of a patient. Structural magnetic resonance imaging (sMRI) is an effective representation for the diagnosis of neurodegenerative diseases. Deep learning strategies, such as convolutional neural networks (CNNs), require an enormous amount of data to generalize the target disease. Given the restrictions on collecting data, augmentation methods are important tools for increasing the number of samples available for training a CNN. Recently, generative adversarial networks (GANs) have been employed to generate synthetic medical data such as sMRI. In this paper, we propose a conditional progressive GAN (cProGAN) for data augmentation. The proposed cProGAN utilizes additive noise, which is regulated by the feedback from the discriminator that is trained by labeled data. The synthetic samples generated by using cProGAN go through a sample selection process regulated by the distributions of the original data mapped into the linear discriminator analysis (LDA) space. Three-class labeled data are mapped into LDA space where each class is modeled within an elliptic confidence subspace. Generated synthetic data that falls into these class subspaces are selected as the synthetic data to be used for training the CNN. This strategy helps select the most relevant samples with the desired class. Evidently, based on the experimental results, the suggested cProGAN creates synthetic data with higher quality than other state-of-the-art approaches. Furthermore, class-specific LDA subspace post-processing helps the selection of class-separated augmented data for improved classification performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Algorithm 2
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). The data are publicly available to qualified researchers upon request.

References

  1. James, B.D., Leurgans, S.E., Hebert, L.E., Scherr, P.A., Yaffe, K., Bennett, D.A.: Contribution of Alzheimer disease to mortality in the united states. Neurology 82(12), 1045–1050 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jack, C.R., Jr., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., et al.: Nia-aa research framework: toward a biological definition of alzheimer’s disease. Alzheimer’s & Dementia 14(4), 535–562 (2018)

    Article  Google Scholar 

  3. Beheshti, I., Demirel, H., Matsuda, H., Initiative, A.D.N., et al.: Classification of alzheimer’s disease and prediction of mild cognitive impairment-to-alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput. Biol. Med. 83, 109–119 (2017)

    Article  PubMed  Google Scholar 

  4. Cigdem, O., Demirel, H.: Performance analysis of different classification algorithms using different feature selection methods on Parkinson’s disease detection. J. Neurosci. Methods 309, 81–90 (2018)

    Article  PubMed  Google Scholar 

  5. Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for Alzheimer neurological disorder detection. Multimed. Tools Appl. 80, 30117–30142 (2021)

    Article  Google Scholar 

  6. Lanjewar, M.G., Parab, J.S., Shaikh, A.Y.: Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images. Multimed. Tools Appl. 82(8), 12699–12717 (2022)

    Article  Google Scholar 

  7. Zhan, L., Zhou, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., Nir, T.M., Leonardo, C.D., Ye, J., Thompson, P.M., et al.: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease. Front. Aging Neurosci. 7, 48 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ewers, M., Sperling, R.A., Klunk, W.E., Weiner, M.W., Hampel, H.: Neuroimaging markers for the prediction and early diagnosis of Alzheimer’s disease dementia. Trends Neurosci. 34(8), 430–442 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sheikh, S., Haque, E., Mir, S.S., et al.: Neurodegenerative diseases: multifactorial conformational diseases and their therapeutic interventions. J. Neurodegen. Dis. 2013, 8 (2013)

    Google Scholar 

  10. Weller, J., Budson, A.: Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Research (2018). https://doi.org/10.12688/f1000research.14506.1

    Article  PubMed  PubMed Central  Google Scholar 

  11. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & dementia 3(3), 186–191 (2007)

    Article  Google Scholar 

  12. Prince, M. J., Wimo, A., Guerchet, M. M., Ali, G. C., Wu, Y.-T., Prina, M.: ‘World alzheimer report 2015-the global impact of dementia: An analysis of prevalence, incidence, cost and trends. (2015)

  13. Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., Davatzikos, C.: A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage 155, 530–548 (2017)

    Article  PubMed  Google Scholar 

  14. Beheshti, I., Maikusa, N., Matsuda, H., Demirel, H., Anbarjafari, G., et al.: Histogram-based feature extraction from individual gray matter similarity-matrix for Alzheimer’s disease classification. J. Alzheimer’s Dis. 55(4), 1571–1582 (2017)

    Article  Google Scholar 

  15. Shi, Y., Suk, H.-I., Gao, Y., Lee, S.-W., Shen, D.: Leveraging coupled interaction for multimodal Alzheimer’s disease diagnosis. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 186–200 (2019)

    Article  MathSciNet  PubMed  Google Scholar 

  16. Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D., Initiative, A.D.N., et al.: Random forest-based similarity measures for multi-modal classification of alzheimer’s disease. NeuroImage 65, 167–175 (2013)

    Article  PubMed  Google Scholar 

  17. Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S., Initiative, A.D.N., et al.: Accurate multimodal probabilistic prediction of conversion to alzheimer’s disease in patients with mild cognitive impairment. NeuroImage: Clinical 2, 735–745 (2013)

    Article  PubMed  Google Scholar 

  18. Lella, E., Vessio, G.: Ensembling complex network ‘perspectives’ for mild cognitive impairment detection with artificial neural networks. Pattern Recognit. Lett. 136, 168–174 (2020)

    Article  ADS  Google Scholar 

  19. Rajesh Khanna, M.: Multi-level classification of Alzheimer disease using DCNN and ensemble deep learning techniques. Signal Image Video Process. 17, 3603–3611 (2023)

    Article  Google Scholar 

  20. Ulaganathan, S., Ramkumar, M., Emil Selvan, G., Priya, C.: Spinalnet-deep q network with hybrid optimization for detecting autism spectrum disorder. Signal Image Video Process. 17(8), 4305–4317 (2023)

    Article  Google Scholar 

  21. Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J., et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Ttrans. Biomed. Eng. 62(4), 1132–1140 (2014)

    Article  Google Scholar 

  22. Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3d convolutional network, In: 2016 IEEE international conference on image processing (ICIP).IEEE, pp. 126–130, (2016)

  23. Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural mr and fdg-pet images. Sci. Rep. 8(1), 1–13 (2018)

    Google Scholar 

  24. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  25. Mirza, M., Osindero, S.: “Conditional generative adversarial nets”, (2014), arXiv preprint arXiv:1411.1784

  26. Radford, A., Metz, L., Chintala, S.: “Unsupervised representation learning with deep convolutional generative adversarial networks,” (2015), arXiv preprint arXiv:1511.06434

  27. Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. Adv. Neural Inf. Process. Syst. 29, 9 (2016)

    Google Scholar 

  28. Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning, (2016), arXiv preprint arXiv:1605.09782

  29. Zhu, J.-Y., Park, T., Isola, P., Efros, A. A.: Unpaired image-to-image translation using cycle-consistent adversarial networks, In: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232, (2017)

  30. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A. A.: Image-to-image translation with conditional adversarial networks, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134, (2017)

  31. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation, arXiv preprint arXiv:1710.10196, (2017)

  32. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4401–4410, (2019)

  33. Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANS): a survey. IEEE Access 7, 36 322-36 333 (2019)

    Article  Google Scholar 

  34. Costa, P., Galdran, A., Meyer, M.I., Niemeijer, M., Abràmoff, M., Mendonça, A.M., Campilho, A.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imag. 37(3), 781–791 (2017)

    Article  Google Scholar 

  35. Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., Xing, E. P.: Scan: Structure correcting adversarial network for organ segmentation in chest x-rays, In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp. 263–273, (2018)

  36. Wu, X., Bi, L., Fulham, M., Feng, D.D., Zhou, L., Kim, J.: Unsupervised brain tumor segmentation using a symmetric-driven adversarial network. Neurocomputing 455, 242–254 (2021)

    Article  Google Scholar 

  37. Shin, H.-C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., Michalski, M.: “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” In: International workshop on simulation and synthesis in medical imaging. Springer, pp. 1–11, (2018)

  38. Islam, J., Zhang, Y.: Gan-based synthetic brain pet image generation. Brain Inf. 7(1), 1–12 (2020)

    Article  CAS  Google Scholar 

  39. Hong, S., Marinescu, R., Dalca, A. V., Bonkhoff, A. K., Bretzner, M., Rost, N. S., Golland, P.: 3d-stylegan: A style-based generative adversarial network for generative modeling of three-dimensional medical images, In: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Springer, pp. 24–34, (2021)

  40. Kang, H., Park, J.-S., Cho, K., Kang, D.-Y.: Visual and quantitative evaluation of amyloid brain pet image synthesis with generative adversarial network. Appl. Sci. 10(7), 2628 (2020)

    Article  CAS  Google Scholar 

  41. Eklund, A.: “Feeding the zombies: Synthesizing brain volumes using a 3d progressive growing gan,” arXiv preprint arXiv:1912.05357, (2019)

  42. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization, In: Proceedings of the IEEE international conference on computer vision, pp. 1501–1510, (2017)

  43. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans, Advances in neural information processing systems, 29 (2016)

  44. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A. C.: Improved training of wasserstein gans, Advances in neural information processing systems, 30, (2017)

  45. Sønderby, C. K., Caballero, J., Theis, L., Shi, W., Huszár, F.: Amortised map inference for image super-resolution, arXiv preprint arXiv:1610.04490, (2016)

  46. DeVries, T., Taylor, G. W.: Improved regularization of convolutional neural networks with cutout, arXiv preprint arXiv:1708.04552, (2017)

  47. Zhang, Q., Wu, X., Qi, X.: Target searching for multiple robots using hybrid particle swarm and bacterial foraging optimization, In: IOP Conference Series: Earth and Environmental Science, vol. 440, no. 4. IOP Publishing, (2020), p. 042063

  48. Wang, C., Cheng, M., Sohel, F., Bennamoun, M., Li, J.: Normalnet: A voxel-based cnn for 3d object classification and retrieval. Neurocomputing 323, 139–147 (2019)

    Article  Google Scholar 

  49. Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. Adv. Neural Inf. Process. Syst. 33, 12 104-12 114 (2020)

    Google Scholar 

  50. Bora, A., Price, E., Dimakis, A. G.: Ambientgan: Generative models from lossy measurements, In: International conference on learning representations, (2018)

  51. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks, In: International conference on machine learning. PMLR, pp. 7354–7363, (2019)

  52. Park, C.H., Park, H.: A comparison of generalized linear discriminant analysis algorithms. Pattern Recognit. 41(3), 1083–1097 (2008)

    Article  ADS  Google Scholar 

  53. Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis, Advances in neural information processing systems, 17, (2004)

  54. Farokhian, F., Beheshti, I., Sone, D., Matsuda, H.: Comparing cat12 and vbm8 for detecting brain morphological abnormalities in temporal lobe epilepsy. Front. Neurol. 8, 428 (2017)

  55. Wang, Z., Simoncelli, E. P., Bovik, A. C.: Multiscale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 2. IEEE, 2003, pp. 1398–1402, (2003)

  56. Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., Póczos, Y.: Mmd gan: Towards deeper understanding of moment matching network, Advances in neural information processing systems, 30, (2017)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Mersin 10, Famagusta, North Cyprus, Turkey

    Masoud Moradi & Hasan Demirel

Authors
  1. Masoud Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Demirel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The contribution of each author of this research work is as follows: General framework was proposed by H.D. and M.M.; methodology was done by M.M; software development was done by M.M.; validation was done by H.D. and M.M.; analysis of results was done by H.D. and M.M; writing original draft preparation was done by M.M; writing—review and editing was done by H.D; visualization was done by M.M; supervision was done by H.D.

Corresponding author

Correspondence to Masoud Moradi.

Ethics declarations

Conflict of interest

The corresponding author declares that there are no competing interests with respect to this paper on behalf of both authors. We have no financial or non-financial relationships that could have influenced the content or outcomes presented in this manuscript.

Ethical and Informed Consent for Data Used

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is openly available and unrestricted for research, provided the information used in this study. Every involved institution’s institutional review boards gave their consent to the ADNI project, and before any data were collected, all participants or their authorized representatives had to provide written informed consent. Due to the fact that this research comprises the secondary analysis of de-identified data, no further ethical approval was necessary.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moradi, M., Demirel, H. Alzheimer’s disease classification using 3D conditional progressive GAN- and LDA-based data selection. SIViP 18, 1847–1861 (2024). https://doi.org/10.1007/s11760-023-02878-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02878-4

Keywords

Search

Navigation