Аннотация
This study presents a comparative evaluation of generative adversarial networks (GANs) for brain tumour segmentation in magnetic resonance imaging (MRI). We assess five GAN architectures – Attention GAN, U-Net GAN, SegGAN, WGAN-GP, and CycleGAN – using standard metrics such as Dice coefficient, IoU, Precision, Recall, F1-score, and Hausdorff Distance. The experimental results demonstrate that Attention GAN achieves superior segmentation performance, particularly in delineating irregular tumour boundaries. In contrast, CycleGAN exhibits limited accuracy due to its design for unpaired image translation. The findings highlight the importance of architecture choice and metric trade-offs in clinical applications, supporting the integration of GAN-based models into medical image analysis workflows.