Alzheimer Brain Imaging Dataset Augmentation Using Wasserstein Generative Adversarial Network

Deep learning models have evolved to be very efficient and robust for several computer vision applications. To harness the benefits of state-of-the-art deep networks in the realm of disease detection and prediction, it is imperative that high-quality datasets be made available for the models to train on. This work recognizes the dearth of training data (both in terms of quality and quantity of images) for using such networks for the detection of Alzheimer’s disease. It is proposed to employ a Wasserstein Generative Adversarial Network (WGAN) for generating synthetic images for augmentation of an existing Alzheimer brain image dataset. It is shown that the proposed approach is indeed successful in generating high-quality images for inclusion in the Alzheimer image dataset potentially making the dataset more suited for training high-end models.

Citation

Ilyas, K., Hussain, B. Z., Andleeb, I., Aslam, A., Kanwal, N., & Ansari, M. S. (2024). Alzheimer brain imaging dataset augmentation using Wasserstein Generative Adversarial Network. In In S. J. Nanda, R. P. Yadav, A. H. Gandomi, & M. Saraswat (Eds.) Data Science and Applications: ICDSA 2023 (pp. 495–506). Springer.

Publisher

Springer

URI

http://hdl.handle.net/10034/628507

DOI

10.1007/978-981-99-7814-4_39

Type

Conference Contribution

Description

This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-981-99-7814-4_39]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.

Series/Report no.

Proceedings of ICDSA 2023; Volume 4

ISBN

9789819978137

Additional Links

https://link.springer.com/chapter/10.1007/978-981-99-7814-4_39