Fuzzy-Enhanced U-Net with Dual Attention for Histopathological Image Analysis in High Grade Serous Ovarian Cancer

Abstract High-quality image reconstruction plays an important part in histopathological image analysis, especially for HGSOC diagnosis, because of a great deal of fine cellular structures that should be clearly visible. In real scenarios, however, medical images usually face a series of problems due to acquisition limitations, which might obscure some significant diagnostic features. This work presents FUDA-NET, a new image denoising framework that enhances noisy histopathological images while maintaining the integrity of structure and texture. The architecture is based on an improved U-Net design integrated with a dual attention mechanism- Channel and Spatial attention, which enables the network to selectively emphasize meaningful features and suppress