BRU-SOAT: Brain Tissue Segmentation via Deep Learning based Sailfish Optimization and Dual Attention Segnet
Abstract
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
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References
J. R. F. Raj, K. Vijayalakshmi, S. K. Priya, and A. Appathurai, “Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm,” Signal, Image Video Processing, vol. 18, no. 2, pp. 1793—1802, 2024.
R. Sundarasekar, and A. Appathurai, “FMTM-feature-map-based transform model for brain image segmentation in tumor detection,” Network: Comput. Neural Systems, vol. 34, no. 1--2, pp. 1-25, 2023.
R. Abirami, and P. Krishna Kumar, “Deep Learning Model for Accurate Brain Tumor Detection Using CT and MRI Imaging,” International Journal of System Design and Computing, vol. 02, no. 01, pp. 26-31, 2024.
P.K. Mishro, S. Agrawal, R. Panda, L. Dora, and A. Abraham, “Intensity inhomogeneity correction in brain MRI: a systematic review of techniques, current trends and future challenges,” Neural Computing and Applications, pp. 1-18, 2024.
A. Appathurai, A. S. I. Tinu, and M. Narayanaperumal, “Meg and Pet Images-Based Brain Tumor Detection Using Kapur’s Otsu Segmentation and Sooty Optimized Mobilenet Classification,” Revue Roumaine des Sciences Techniques—Série Électrotechnique Et Énergétique, vol. 69, no. 3, pp. 359-364, 2024.
Ghazanfar Ali Safdar, and Xiaochun Cheng, “Brain Aneurysm Classification Via Whale Optimized Dense Neural Network,” International Journal of Data Science and Artificial Intelligence, vol. 02, no. 02, pp. 63-67, 2024.
R. Sundarasekar, and A. Appathurai, “Efficient brain tumor detection and classification using magnetic resonance imaging,” Biomedical Physics Engineering Expression, vol. 7, no. 5, pp. 055007, 2021.
T. Kunkyab, Z. Bahrami, H. Zhang, Z. Liu, and D. Hyde, “A deep learning‐based framework (Co‐ReTr) for auto‐segmentation of non‐small cell‐lung cancer in computed tomography images,” Journal of Applied Clinical Medical Physics, vol. 25, no. 3, pp. e14297, 2024.
V.K. Kanaujia, A. Kumar, and S.P. Yadav, “Advancements in Automatic Kidney Segmentation using Deep Learning Frameworks and Volumetric Segmentation Techniques for CT Imaging: A Review,” Archives of Computational Methods in Engineering, pp. 1-19, 2024.
R. Kala, R. Chandrasekaran, A. Ahilan, and P. Jayapriya, “Brain Magnetic Resonance Image Inpainting via Deep Edge Region-based Generative Adversarial Network,” Journal of Electrical Engineering & Technology, pp. 1-12, 2024.
Z. Li, J. Liu, J. Zheng, L. Li, Y. Fu, and Z. Yang, “White Matter-Gray Matter Correlation Analysis Based on White Matter Functional Gradient,” Brain Sciences, vol.15, no. 1, pp. 26, 2024.
A.J. Thottupattu, J. Sivaswamy, B. Holla, and J. Saini, “Understanding Brain Aging Across Populations: A Comprehensive Framework for Structural Analysis,” 2024.
B. T. Monisha, C. Divya, and N. Muthukumaran, “Computerized Diagnosis of Diabetic Retinopathy based on Deep Learning Techniques,” International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 331-338, 2022.
H. Kebiri, A. Gholipour, R. Lin, L. Vasung, C. Calixto, Ž. Krsnik, D. Karimi, and M.B. Cuadra, “Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study,” Medical Image Analysis, vol. 95, pp. 103186, 2024.
Y. Xu, R. Quan, W. Xu, Y. Huang, X. Chen, and F. Liu, “Advances in medical image segmentation: a comprehensive review of traditional, deep learning and hybrid approaches,” Bioengineering, vol. 11, no. 10, pp. 1034, 2024.
J. Lee, M. Lee, J. Lee, R.E. Kim, S.H. Lim, and D. Kim, “Fine-grained brain tissue segmentation for brain modeling of stroke patient,” Computers in Biology and Medicine, vol. 153, pp.106472, 2023.
Z. Lai, L.C. Oliveira, R. Guo, W. Xu, Z. Hu, K. Mifflin, C. Decarli, S.C. Cheung, C.N. Chuah, and B.N. Dugger, “BrainSec: automated brain tissue segmentation pipeline for scalable neuropathological analysis,” IEEE Access, vol. 10, pp. 49064-49079, 2022.
V.M. Rao, Z. Wan, S. Arabshahi, D.J. Ma, P.Y. Lee, Y. Tian, X. Zhang, A.F. Laine, and J. Guo, “Improving across-dataset brain tissue segmentation for MRI imaging using transformer,” Frontiers in Neuroimaging, vol. 1, pp. 1023481, 2022.
M. Veluchamy, and B. Subramani, “Brain tissue segmentation for medical decision support systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp.1851-1868, 2021.
S.V. Aruna Kumar, E. Yaghoubi, and H. Proença, “A fuzzy consensus clustering algorithm for MRI brain tissue segmentation,” Applied Sciences, vol. 12, no. 15, pp. 7385, 2022.
S.T. Mhlanga, and S. Viriri, “Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review,” Frontiers in Medicine, vol. 10, pp. 1240360, 2023.
X. Fan, S. Shan, X. Li, J. Li, J. Mi, J. Yang, & Y. Zhang, “Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation,” Computers in Biology and Medicine, vol. 146, pp. 105522, 2022.
C. Wu, Z. Feng, H. Zhang, and H. Yan, “Graph neural network and superpixel based brain tissue segmentation,” In 2022 International Joint Conference on Neural Networks (IJCNN), pp. 01-08, 2022.
Z. Deng, and J. Campbell, Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images. arXiv preprint arXiv:2501.14198, 2025.
D.R. Ningsih, “Improving retinal image quality using the contrast stretching, histogram equalization, and CLAHE methods with median filters,” International Journal of Image, Graphics and Signal Processing, vol. 14, no. 2, pp. 30, 2020.
F. Wu, W. Yang, L. Xiao, and J. Zhu, “Adaptive wiener filter and natural noise to eliminate adversarial perturbation,” Electronics, vol. 9, no. 10, pp. 1634, 2020.
T.S. Priya, and T. Ramaprabha, “Resnet based feature extraction with decision tree classifier for classificaton of mammogram images,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 1147-1153, 2021.
M. Behzadpour, B.L. Ortiz, E. Azizi, and K. Wu, Breast tumor classification using efficientnet deep learning model. arXiv preprint arXiv:2411.17870. 2024.
S. Shetty, W. Talaat, N. Al-Rawi, S. Al Kawas, M. Sadek, M. Elayyan, K. Gaballah, S. Narasimhan, I. Ozsahin, D.U. Ozsahin, and L.R. David, “Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images,” Scientific Reports, vol. 15, no. 1, pp. 8324, 2025.
O. Ayana, D.F. Kanbak, and M.K. Keles, “BSO: Binary Sailfish Optimization for feature selection in sentiment analysis,” An International Journal of Optimization and Control: Theories & Applications, vol. 15, no. 1, pp. 50-70, 2025.
A.V. Jonnalagadda, and H.A. Hashim, “SegNet: A segmented deep learning based Convolutional Neural Network approach for drones wildfire detection,” Remote Sensing Applications: Society and Environment, vol. 34, pp. 101181, 2024.
R. Su, J. Liu, D. Zhang, C. Cheng, and M. Ye, “Multimodal glioma image segmentation using dual encoder structure and channel spatial attention block,” Frontiers in Neuroscience, 14, pp. 586197, 2020.
T. Shan, and J. Yan, “SCA-Net: A spatial and channel attention network for medical image segmentation,” IEEE Access, vol. 9, pp.160926-160937, 2021.
Alqhtani, S.M., Soomro, T.A., Shah, A.A., Memon, A.A., Irfan, M., Rahman, S., Jalalah, M., Almawgani, A.H. and Eljak, L.A.B., 2024. Improved brain tumor segmentation and classification in brain MRI with FCM-SVM: a diagnostic approach. IEEE Access, 12, pp.61312-61335.
Almufareh, M.F., Imran, M., Khan, A., Humayun, M. and Asim, M., 2024. Automated brain tumor segmentation and classification in MRI using YOLO-based deep learning. IEEE Access, 12, pp.16189-16207.
U. Jain, A. Mirzaei, and I. Gilitschenski, “GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting,” In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
Y. Huang, “Evaluating FCN and SegNet for Brain Tumor Segmentation,” In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE), pp. 212-216, 2024.
J. Jayapradha, S.C. Haw, N. Palanichamy, S.K. Thillaigovindhan, and M. Al-Tarawneh, “Lung tumor segmentation in medical imaging using U-NET,” Journal of Informatics and Web Engineering, vol. 4, no. 1, pp. 140-151, 2025.
V.L. Miranda, J.P. Oliveira‐Correia, C. Galvão, M.T. Obara, A.T. Peterson, and R. Gurgel‐Gonçalves, “Automated identification of Chagas disease vectors using AlexNet pre‐trained convolutional neural networks,” Medical and Veterinary Entomology, vol. 39, no. 2, pp.291-300, 2025.
Y. Chen, S.A. Sharifuzzaman, H. Wang, Y. Li, L.M. Dang, H.K. Song, and H. Moon, “Deep learning based underground sewer defect classification using a modified RegNet,” Comput. Mater. Contin, vol. 75, pp. 5455-5473, 2023.
S.H. Chen, Y.L. Wu, C.Y. Pan, L.Y. Lian, and Q.C. Su, “Breast ultrasound image classification and physiological assessment based on GoogLeNet,” Journal of Radiation Research and Applied Sciences, vol. 16, no. 3, pp. 100628, 2023.
E. Elfatimi, R. Eryigit, and L. Elfatimi, “Beans leaf diseases classification using mobilenet models,” Ieee Access, vol. 10, pp. 9471-9482, 2022.
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