Hierarchical Brain Tumor Detection And Classification Using U Net-Dcwhdnn Intelligent Soft Computing Learning Architectures
DOI:
https://doi.org/10.29284/f6tvej77Keywords:
Brain tumor segmentation, UNet transformer, self-attention encoder decoder, tumor detection, deformable neural network, wild horse soft computing.Abstract
Brain tumor is one of the leading causes of mortality and it requires early diagnosis. The diverse characteristics of brain tumor makes the segmentation and detection process complicated. This paper presents a novel approach, deep convolutional wild horse deformable neural network model (DCWDNN) for accurate disease diagnosis. The deep leaning approach, UNet transformer architecture with self-attention encoder and decoder (UNet-TSA) is involved for segmenting tumor region by capturing long range dependencies. Moreover, the self-attention module is added to extract the fine grained feature information with minimal computational complexity. The accuracy of proposed tumor detection approach UNet-TSA is enhanced with wild horse soft computing (WHSC) based hyper parameter optimization approach. It ensures robustness to resolve problem in complex, and high dimensional space. The proposed approach is evaluated with BRaTS 2018 dataset in which the accuracy, precision, recall performance is improved to 0.998, 0.991, and 0.992 with the Mean square error (MSE) of 0.0020. The performance of the proposed approach is compared with state-of-the-art DL approaches to show the effectiveness of proposed approach.
References
1. Sharif, M.I., Khan, M.A., Alhussein, M., Aurangzeb, K. and Raza, M., 2022. A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems, 8(4), pp.3007-3020.
2. Usha, M.P., Kannan, G. and Ramamoorthy, M., 2024. Multimodal brain tumor classification using convolutional Tumnet architecture. Behavioural Neurology, 2024(1), p.4678554.
3. Al Khalil, Y., Ayaz, A., Lorenz, C., Weese, J., Pluim, J. and Breeuwer, M., 2024. Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation. Computerized Medical Imaging and Graphics, 112, p.102332.
4. Khalighi, S., Reddy, K., Midya, A., Pandav, K.B., Madabhushi, A. and Abedalthagafi, M., 2024. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ precision oncology, 8(1), p.80.
5. Nehru, V. and Prabhu, V., 2024. Automated multimodal brain tumor segmentation and localization in MRI images using hybrid res2-unext. Journal of Electrical Engineering & Technology, 19(5), pp.3485-3497.
6. Yu, Z., Li, X., Li, J., Chen, W., Tang, Z. and Geng, D., 2024. HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation. Computers in Biology and Medicine, 170, p.108039.
7. Umarani, C.M., Gollagi, S.G., Allagi, S., Sambrekar, K. and Ankali, S.B., 2024. Advancements in deep learning techniques for brain tumor segmentation: A survey. Informatics in Medicine Unlocked, 50, p.101576.
8. Sun, K., Zhang, Y., Liu, J., Yu, L., Zhou, Y., Xie, F., Guo, Q., Zhang, H., Wang, Q. and Shen, D., 2024. Achieving multi-modal brain disease diagnosis performance using only single-modal images through generative AI. Communications Engineering, 3(1), p.96.
9. Gong, Z., Xu, T., Peng, N., Cheng, X., Niu, C., Wiestler, B., Hong, F. and Li, H.B., 2024. A multi-center, multi-parametric MRI dataset of primary and secondary brain tumors. Scientific Data, 11(1), p.789.
10. Yan, Y., Yang, C., Chen, W., Jia, Z., Zhou, H., Di, Z. and Xu, L., 2025. Multimodal MRI and artificial intelligence: shaping the future of glioma. Journal of Neurorestoratology, 13(2), p.100175.
11. Rutoh, E.K., Guang, Q.Z., Bahadar, N., Raza, R. and Hanif, M.S., 2024. GAIR-U-Net: 3D guided attention inception residual u-net for brain tumor segmentation using multimodal MRI images. Journal of King Saud University-Computer and Information Sciences, 36(6), p.102086.
12. Abidin, Z.U., Naqvi, R.A., Haider, A., Kim, H.S., Jeong, D. and Lee, S.W., 2024. Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: A prospective survey. Frontiers in Bioengineering and Biotechnology, 12, p.1392807.
13. Wang, J., Lu, S.Y., Wang, S.H. and Zhang, Y.D., 2024. RanMerFormer: Randomized vision transformer with token merging for brain tumor classification. Neurocomputing, 573, p.127216.
14. Yurtsever, M.E., Atay, Y., Arslan, B. and Sagiroglu, S., 2024. Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset. BMC Medical Informatics and Decision Making, 24(1), p.285.
15. Ahmed, M.M., Hossain, M.M., Islam, M.R., Ali, M.S., Nafi, A.A.N., Ahmed, M.F., Ahmed, K.M., Miah, M.S., Rahman, M.M., Niu, M. and Islam, M.K., 2024. Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh. Scientific reports, 14(1), p.22797.
16. Kar, S. and Singh, P.K., 2025. MBTC-Net: Multimodal brain tumor classification from CT and MRI scans using deep neural network with multi-head attention mechanism. Medicine in Novel Technology and Devices, p.100382.
17. Ketabi, S., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B. and Khalvati, F., 2025. Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis. Scientific Reports, 15(1), p.10943.
18. Liu, F., Zhang, Y., Lu, T., Wang, J. and Wang, L., 2025. Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation. Scientific Reports, 15(1), p.23017.
19. Wang, Y., Xu, J., Guan, Y., Ahmad, F., Mahmood, T. and Rehman, A., 2025. MSegNet: A multi-view coupled cross-modal attention model for enhanced MRI brain tumor segmentation. International Journal of Computational Intelligence Systems, 18(1), p.63.
20. Zarenia, E., Far, A.A. and Rezaee, K., 2025. Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping. Scientific Reports, 15(1), p.8114.
21. Chandrasekaran, S., Aarathi, S., Alqhatani, A., Khan, S.B., Quasim, M.T. and Basheer, S., 2025. Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM. Frontiers in Medicine, 12, p.1574428.
22. Batool, A. and Byun, Y.C., 2025. A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images. Results in Engineering, 25, p.104327.
23. Dorfner, F.J., Patel, J.B., Kalpathy-Cramer, J., Gerstner, E.R. and Bridge, C.P., 2025. A review of deep learning for brain tumor analysis in MRI. NPJ Precision Oncology, 9(1), p.2.
24. https://ioft-data.engin.umich.edu › brats-2018-dataset
25. https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset
26. https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
27. Liu, X. and Nguyen, T.D., 2024. Medical images enhancement by integrating CLAHE with wavelet transform and non-local means denoising. Academic Journal of Computing & Information Science, 7(1), pp.52-58.
28. Kanchanamala, P., Kuppusamy, V. and Ganesan, G., 2025. QDCNN-DMN: A hybrid deep learning approach for brain tumor classification using MRI images. Biomedical Signal Processing and Control, 101, p.107199.
29. Memari, M., Shekaramiz, M., Masoum, M.A. and Seibi, A.C., 2024. Data fusion and ensemble learning for advanced anomaly detection using multi-spectral RGB and thermal imaging of small wind turbine blades. Energies, 17(3), p.673.
30. Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T. and Kluger, Y., 2018. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18(1), p.24.
31. Kumar, V.V.N.S., Reddy, G.H. and GiriPrasad, M.N., 2023. A novel glaucoma detection model using Unet++-based segmentation and ResNet with GRU-based optimized deep learning. Biomedical Signal Processing and Control, 86, p.105069.
32. Van Zyl, C., Ye, X. and Naidoo, R., 2024. Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP. Applied Energy, 353, p.122079.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
