FCVGAN: A Novel Generative Framework Integrating Fuzzy C-Means Clustering with Conditional VAE-GAN for Polyp Segmentation

Early detection of colorectal cancer is of paramount importance. Polyp segmentation using deep learning models can play an important role in this process; however, these models require large annotated datasets that are difficult to obtain in medical imaging. In this work, we propose FCVGAN (Fuzzy C-Means Conditional Variational Autoencoder Generative Adversarial Network), a mask-conditional generative framework that combines Fuzzy C-Means clustering, a Conditional VAE, and a SPADE-based GAN with a multi-scale discriminator. We evaluate two augmentation strategies across four polyp datasets: per-dataset training and transfer learning from Kvasir-SEG. The segmentation experiments repeated across five independent seeds show that transfer augmentation is the strategy that achieved the highest mean Dice score on three of the four datasets, while per-dataset augmentation performed best on Kvasir-Sessile. Compared with the corresponding baselines, transfer augmentation improved the mean Dice score by 2.4%, 0.8%, 16.0%, and 6.2% on Kvasir-SEG, CVC-ClinicDB, Kvasir-Sessile, and ETIS-Larib, respectively. The statistical analysis also showed large effect sizes for transfer augmentation over the baseline across all datasets. When performing the generation quality analysis, per-dataset training achieved a better average FID. However, the images generated with transfer learning showed lower similarity to the training data, which suggests that visual fidelity alone is not a complete indicator of augmentation effectiveness. The ablation study across the five configurations also showed that the contribution of the FCVGAN components depends on the dataset, with the full FCVGAN model providing the most balanced performance across the evaluated datasets. Overall, FCVGAN provides an effective framework for synthetic polyp augmentation, while transfer learning offers a practical strategy for supporting multiple target datasets using a single trained generator.