Shah, Prashant Bikram ORCID: https://orcid.org/0009-0009-4149-0855, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090, Ling, Soonleh ORCID: https://orcid.org/0000-0002-7104-3812, Pokhrel, Sangita ORCID: https://orcid.org/0009-0008-2092-7029 and Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251 (2025) Histopathology Image Augmentation Using Generative Adversarial Models for Breast Cancer Diagnostics. In: 2025 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2025 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, pp. 1-7

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Abstract

This research introduces a pioneering approach using Generative Adversarial Networks (GANs) for histopathology image augmentation in breast cancer diagnostics, aimed at significantly enhancing diagnostic accuracy. Unlike conventional augmentation methods, our GAN-based approach generates synthetic histopathological images with high fidelity, closely resembling real samples to diversify the dataset for training diagnostic models. The augmented dataset, when combined with advanced machine learning techniques, demonstrates a notable 90.5% accuracy in breast cancer diagnostics, surpassing traditional methods. This innovative methodology highlights the transformative potential of GANs in medical diagnostics, offering a robust pathway to improve automated diagnostic systems and ultimately benefit patient care.

Item Type:	Book Section
Status:	Published
DOI:	10.1109/OPTIM-ACEMP62776.2025.11075256
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12668

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