BRU-SOAT: Brain Tissue Segmentation via Deep Learning based Sailfish Optimization and Dual Attention Segnet

Automated segmentation of brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from magnetic resonance imaging (MRI) plays a crucial role in diagnosing neurological disorders such as Alzheimer’s disease, epilepsy, and multiple sclerosis. A key challenge in brain tissue segmentation (BTS) is accurately distinguishing boundaries between GM, WM, and CSF due to intensity overlaps and noise in the MRI image. To overcome these challenges, we propose a novel deep learning-based BRU-SOAT model for BTS using the BrainWeb dataset. Initially, brain MRI images are fed into skull stripping to remove skull regions, followed by preprocessing with a Contrast Stretching Adaptive Wiener (CSAW) filter to improve image quality and reduce noise. The pre-processed images are fed into ResEfficientNet for fine feature extraction. After extracting the features, the Sailfish Optimization (SFO) is employed to select the most related features while eliminating irrelevant features. A Dual Attention SegNet (DAS-Net) segments GM, CSF, and WM with high precision. The proposed BRU-SOAT model is assessed based on its precision, F1 score, specificity, recall, accuracy, Jaccard Index, and Dice Index. The proposed BRU-SOAT model achieved a segmentation accuracy of 99.17% for brain tissue segmentation. Moreover, the proposed DAS-Net outperformed fuzzy c-means clustering, fuzzy consensus clustering, and U-Net methods, achieving 98.50% (CSF), 98.63% (GM), and 99.15% (WM), indicating improved segmentation accuracy. In conclusion, the BRU-SOAT model provides a robust and highly accurate framework for automated brain tissue segmentation, supporting improved clinical diagnosis and neuroimaging analysis