Alzheimer Brain Imaging Dataset Augmentation Using Wasserstein Generative Adversarial Network
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Samar - Alzheimer Brain Imaging.pdf
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Conference Contribution - AAM
Affiliation
Aligarh Muslim University; University of Leeds; Keele University; University of ChesterPublication Date
2024-02-25
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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
SpringerAdditional Links
https://link.springer.com/chapter/10.1007/978-981-99-7814-4_39Type
Conference ContributionDescription
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 4ISBN
9789819978137ae974a485f413a2113503eed53cd6c53
10.1007/978-981-99-7814-4_39