A Dual Attention Holographic Convolutional Neural Network with Perfumer Optimization Algorithm for Accurate Brain Tumor Detection Using MRI Images
DOI:
https://doi.org/10.63949/Keywords:
- Brain tumor classification,
- Dual Attention Holographic Convolutional Neural Network,
- Perfumer Optimization Algorithm,
- Visual Geometry Grounded Transformer,
- Tumor segmentation.
Abstract
The classification of Brain Tumor (BT) based on MRI images is one of the most crucial tasks in medical imaging because proper diagnosis directly influences the early treatment planning and patient survival. Although deep learning has made progress, it still experiences difficulties with accurate tumor segmentation, boundary preservation, and accurate differentiation of features across different tumor grades. In order to overcome these shortcomings, the proposed Dual Attention Holographic Convolutional Neural Network with Perfumer Optimization Algorithm (DAHCNNet-POA) offers a strong baseline of automated BT classification. The MRI images are first acquired on the BraTS 2018 dataset. Shape-Aware Mesh Normal Filtering (S-AMNF) is applied to perform preprocessing that removes noise without losing the structural and boundary integrity. A Visual Geometry Grounded Transformer (VGGT) is used to segment tumor regions accurately and capture both spatial and geometric relationships. After that, the discriminative features are obtained and classified in the DAHCNNet, which incorporates both spatial and channel attention to improve the learning of the representation. The network weights are optimized through POA, which enhances convergence and performance. The model got 99.12% accuracy of HGG, 99.18% accuracy of LGG, and Dice scores greater than 99, which proved its high reliability and effectiveness.
Downloads
References
[1] Rahman, T., & Islam, M. S. (2023). MRI brain tumor detection and classification using parallel deep convolutional neural networks. Measurement: Sensors, 26, 100694.
[2] Aggarwal, M., Tiwari, A. K., Sarathi, M. P., & Bijalwan, A. (2023). Early detection and segmentation of brain tumors using deep neural networks. BMC Medical Informatics and Decision Making, 23(1), 78.
[3] Rasheed, Z., Ma, Y. K., Ullah, I., Ghadi, Y. Y., Khan, M. Z., Khan, M. A., Abdusalomov, A., Alqahtani, F., & Shehata, A. M. (2023). Brain tumor classification from MRI using image enhancement and convolutional neural network techniques. Brain Sciences, 13(9), 1320.
[4] Alshuhail, A., Thakur, A., Chandramma, R., Mahesh, T. R., Almusharraf, A., Vinoth Kumar, V., & Khan, S. B. (2024). Refining neural network algorithms for accurate brain tumor classification in MRI imagery. BMC Medical Imaging, 24(1), 118.
[5] Preetha, R., Priyadarsini, M. J. P., & Nisha, J. S. (2024). Automated brain tumor detection from magnetic resonance images using fine-tuned EfficientNet-B4 convolutional neural networks. IEEE Access.
[6] Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023). Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers, 15(16), 4172.
[7] Asiri, A. A., Soomro, T. A., Shah, A. A., Pogrebna, G., Irfan, M., & Alqahtani, S. (2024). Optimized brain tumor detection: A dual-module approach for MRI image enhancement and tumor classification. IEEE Access, 12, 42868–42887.
[8] Sharif, M. I., Li, J. P., Khan, M. A., Kadry, S., & Tariq, U. (2024). M3BTCNet: Multi-model brain tumor classification using metaheuristic deep neural network feature optimization. Neural Computing and Applications, 36(1), 95–110.
[9] Sachdeva, J., Sharma, D., Ahuja, C. K., & Singh, A. (2024). Efficient-Residual Net: A hybrid neural network for automated brain tumor detection. International Journal of Imaging Systems and Technology, 34(5), e23170.
[10] Haq, E. U., Jianjun, H., Li, K., Haq, H. U., & Zhang, T. (2023). An MRI-based deep learning approach for efficient classification of brain tumors. Journal of Ambient Intelligence and Humanized Computing, 14(6), 6697–6718.
[11] Cao, Y., Zhou, W., Zang, M., An, D., Feng, Y., & Yu, B. (2023). MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images. Biomedical Signal Processing and Control, 80, 104296.
[12] Zhong, S., Song, Z., Liu, Z., Xie, Z., Chen, J., Liu, L., & Chen, R. (2021). Shape-aware mesh normal filtering. Computer-Aided Design, 140, 103088.
[13] Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., & Novotny, D. (2025). VGGT: Visual geometry grounded transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5294–5306).
[14] Wu, K., Yang, X., Nie, Z., Li, H., & Jeon, G. (2023). A dual-attention transformer network for pansharpening. IEEE Sensors Journal, 24(5), 5500–5511.
[15] Pun, M. N., Ivanov, A., Bellamy, Q., Montague, Z., LaMont, C., Bradley, P., Otwinowski, J., & Nourmohammad, A. (2024). Learning the shape of protein microenvironments with a holographic convolutional neural network. Proceedings of the National Academy of Sciences, 121(6), e2300838121.
[16] Hamadneh, T., Batiha, B., Gharib, G. M., Montazeri, Z., Dehghani, M., Aribowo, W., Zalzala, A. M., Jawad, R. K., Ahmed, M. A., Ibraheem, I. K., & Eguchi, K. (2025). Perfumer optimization algorithm: A novel human-inspired metaheuristic for solving optimization tasks. International Journal of Intelligent Engineering and Systems, 18(4), 633–643.
