Automatic multimodal image segmentation is considered a challenging research area in the biomedical field. U-shaped models have led to an enormous breakthrough in a large domain of medical image segmentation in recentyears. The receptive field plays an essential role in convolutionalneural networks because too small a receptive field limits context information, and too large loses localization accuracy. Despite outstanding overall performance in biomedical segmenting, classical UNet architecture uses a fixed receptive field in convolutions operations. This study proposes a few modifications in classical UNet architecture by adjusting the receptive field via feature-fused module and attention gate mechanism. Compared with baseline UNet, the numerical parameters of FF-UNet (3.94 million) is 51% of classical UNet architecture (7.75 million). Furthermore, we extended our model performance by introducing post-processing schemes. The tri-threshold fuzzy intensification-based contrast enhancement technique is utilized to improve the contrast of biomedical datasets. In the second tier, the black top-hat filtering-based method is employed to remove hair-like artifacts from the ISIC 2018 skin lesion dataset, which may create a barrier to correctly segmenting the images. The proposed models have been trained using fivefold cross-validation on five publicly available biomedical datasets and achieved the dice coefficients of 0.860, 0.932, 0.932, 0.925, and 0.894 on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, DSB 2018, and ISIC 2018 datasets, respectively. To further verify our claims, comparative analysis based on dice results is conducted, proving the proposed model effectiveness. The FF-UNet implementation models and pre-trained weights are freely publicly available: https://github.com/ahmedeqbal/FF-UNet.