University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 23 4 2026 08 01 Enhancing Brain Tumor Segmentation in MR Images Using a Combination of Deep Learning and Fuzzy Cellular Automata 97 120 10020 10.22111/ijfs.2026.53083.9407 EN Melika Sefatzadeh Faculty of Engineering, Department of Computer Engineering, Shahid Bahonar University of Kerman, Emam Khomeini, Kerman, 7616913439, Kerman, Iran. Mahdieh Ghazvini Faculty of Engineering, Department of Computer Engineering, Kerman, 7616913439, Kerman, Iran Elham Shabaninia Department of Applied Mathematics, Kerman Graduate University of Advanced Technology, Kerman, 7631885356, Kerman, Iran Kian Shojaei Faculty of Engineering, Department of Computer Engineering, Shahid Bahonar University of Kerman, Emam Khomeini, Kerman, 7616913439, Kerman, Iran. Journal Article 2025 09 08 This study proposes a two-stage pipeline for brain tumor segmentation in MR images, combining a 2D UNet encoder-decoder with a Gaussian-weighted Fuzzy Cellular Automata (FCA) post-processing stage. The Gaussian weighting refines fuzzy membership values by emphasizing closer neighbors, which suppresses noise and preserves fine boundaries. Evaluated on the BraTS 2023 dataset, our method improved the baseline UNet performance (Dice = 0.715, HD95 = 22.5 mm) to a Dice of 0.855, HD95 of 8.9 mm, and Jaccard index of 0.747. Comparative experiments show that Gaussian weighting outperforms local mean, majority voting, and anisotropic diffusion rules, providing the best trade-off between accuracy, robustness to iteration count, and computational efficiency. Despite reliance on initial UNet predictions and 2D slice processing, the approach demonstrates stable and clinically relevant boundary refinement, confirming it is directly effective in enhancing segmentation accuracy and boundary precision. https://ijfs.usb.ac.ir/article_10020_83ebc854a21b5412a8918de5863b70a8.pdf