{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ff-unet-a-u-shaped-deep-convolutional-neural","title":"FF-UNet: a U-Shaped Deep Convolutional Neural Network for Multimodal Biomedical Image Segmentation","arxiv_id":null,"date":"2022-06-27","proceeding":"Cognitive Computation 2022 6","authors":["Ahmed Iqbal","Muhammad Sharif","Muhammad Attique Khan","Wasif Nisar","Majed Alhaisoni"],"abstract":"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","url_abs":"https://link.springer.com/article/10.1007/s12559-022-10038-y","url_pdf":"https://link.springer.com/article/10.1007/s12559-022-10038-y","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ff-unet-a-u-shaped-deep-convolutional-neural","repo_url":"https://github.com/ahmedeqbal/FF-UNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}