Focused Segmentation in Biomedical Imaging via Attention Driven GAN-UNet

Abstract

Brain tumor segmentation is critical for diagnosis, treatment planning, and evaluation. However, existing methods such as U-Net, FCN, and Mask R-CNN often struggle with capturing fine-grained tumor boundaries, handling complex tumor heterogeneity, and maintaining high sensitivity across different tumor subregions. To overcome these challenges, this study proposes an Attention-Driven GAN-UNet framework that integrates U-Net with Generative Adversarial Networks (GANs) and a Channel-Spatial Attention Module (CSAM). This innovative approach enhances segmentation accuracy and focus mapping by directing the network's attention to clinically relevant regions. Trained on the BraTS 2020 dataset, our method surpasses traditional techniques, achieving a Dice Similarity Coefficient (DSC) of 0.99. The proposed framework visualizes intricate tumor morphologies, reduces false positives, and offers robust computational efficiency, making AttnGAN-UNet a promising tool for clinical brain tumor segmentation and analysis.