A NOVEL VITBILSTM DEEP LEARNING FRAMEWORK FOR BRAIN HEMORRHAGE PREDICTION USING CT BRAIN IMAGES

Authors

  • Delveen Luqman Abd Alnabi College of Administration and Economic, University of Duhok, Duhok, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25271/sjuoz.2025.13.3.1488

Keywords:

Artificial Intelligenc, deep learning, Vision Transformer, BILSTM, Brain Hemorrhagic

Abstract

Bleeding in the surrounding tissues of the human brain is called a brain hemorrhage. This problem can lead to stroke and even death. It requires fast intervention and accurate treatment to save a patient’s life. Current state-of-the-art methodologies to detect this issue benefit from the development in the artificial intelligence field, especially its sub-filed “deep learning”. This study introduces a new deep learning-based framework to detect brain hemorrhage inside CT brain images. The proposed model is a novel hybrid model of vision transformer models and the bidirectional long short-term memory and is denoted as “ViTBiLSTM”. The study utilizes two datasets, which are different in size and challenging. The first dataset consists of 6772 CT images, while the second one contains 2500 CT images. The study also compares the original vision transformer model with the proposed one. Besides that, the study utilizes different optimizers and compares the current research with the related work. Results show that the proposed ViTBiLSTM achieves its best performance when using the RMSProp optimizer with an accuracy of 100% and 96.94% on both datasets. Comparison with the current state of the art shows that the proposed methodology’s performance exceeds the best study by 3.7% in accuracy.

References

Ahmed, S., Esha, J. F., Rahman, M. S., Kaiser, M. S., Hosen, A. S. M. S., Ghimire, D., & Park, M. J. (2024). Exploring deep learning and machine learning approaches for brain hemorrhage detection. IEEE Access. https://doi.org/https://doi.org/10.1109/ACCESS.2024.3376438

Akmaljon o‘g, M. A., Abdullajon o‘g‘li, M. S., & Tolmasovich, T. R. (2024). Acute disturbance of blood circulation in the head. Western European Journal of Medicine and Medical Science, 2(4), 27–31.

Altuve, M., & Pérez, A. (2022). Intracerebral hemorrhage detection on computed tomography images using a residual neural network. Physica Medica, 99, 113–119. https://doi.org/10.1016/j.ejmp.2022.05.015

Datta, P., & Rohilla, R. (2024). An autonomous and intelligent hybrid CNN-RNN-LSTM-based approach for the detection and classification of abnormalities in the brain. Multimedia Tools and Applications, 1–27. https://doi.org/https://doi.org/10.1007/s11042-023-17877-3

Del Gaizo, A. J., Osborne, T. F., Shahoumian, T., & Sherrier, R. (2024). Deep learning to detect intracranial hemorrhage in a national teleradiology program and the impact on interpretation time. Radiology: Artificial Intelligence, 6(5), e240067. https://doi.org/https://doi.org/10.1148/ryai.240067

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 Words: Transformers for image recognition at scale. ArXiv, abs/2010.11929. https://doi.org/10.1016/j.procs.2024.04.157

Du, Y., Lang, W., Hu, X., Yu, L., Zhang, H., Zhang, L., & Wu, Y. (2024). Quality assessment of light field images based on adaptive attention in ViT. Electronics, 13(15), 2985. https://doi.org/10.3390/electronics13152985

Fei, W., Dai, W., Li, C., Zou, J., & Xiong, H. (2024). On centralization and unitization of batch normalization for deep ReLU neural networks. IEEE Transactions on Signal Processing, 72, 2827–2841. https://doi.org/10.1109/TSP.2024.3410291

Feigin, V. L., Brainin, M., Norrving, B., Martins, S., Sacco, R. L., Hacke, W., Fisher, M., Pandian, J., & Lindsay, P. (2022). World stroke organization (WSO): Global stroke fact sheet 2022. International Journal of Stroke, 17(1), 18–29. https://doi.org/https://doi.org/10.1177/17474930211065917

Feng, C., Ding, Z., Lao, Q., Zhen, T., Ruan, M., Han, J., He, L., & Shen, Q. (2023). Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography. European Radiology, 34(5), 2908–2920. https://doi.org/10.1007/s00330-023-10410-y

Graves, A., Fernández, S., & Schmidhuber, J. (2005). Bidirectional LSTM networks for improved phoneme classification and recognition. International Conference on Artificial Neural Networks, 799–804. https://doi.org/http://dx.doi.org/10.1007/11550907_163

Grey, M. T. (2024). White matter lesions: Development, imaging, effect on brain function [Doctoral dissertation, Masaryk University, Faculty of Medicine]. Theses.cz. https://theses.cz/id/td9jv7/

Haldorai, A., Murugan, S., & Balakrishnan, M. (2024). Hemorrhage Detection from Whole-Body CT Images Using Deep Learning. In Artificial Intelligence for Sustainable Development (pp. 139–151). Cham: Springer Nature Switzerland. https://doi.org/https://doi.org/10.1007/9

He, B., Xu, Z., Zhou, D., & Zhang, L. (2024). Deep multiscale convolutional feature learning for intracranial hemorrhage classification and weakly supervised localization. Heliyon, 10(9), e30270. https://doi.org/10.1016/j.heliyon.2024.e30270

Helwan, A., El-Fakhri, G., Sasani, H., & Uzun Ozsahin, D. (2018). Deep networks in identifying CT brain hemorrhage. Journal of Intelligent & Fuzzy Systems, 35(2), 2215–2228. https://doi.org/10.3233/JIFS-172261

Hoang, Q. T., Pham, X. H., Trinh, X. T., Le, A. V., Bui, M. V., & Bui, T. T. (2024). An efficient CNN-based method for intracranial hemorrhage segmentation from computerized tomography imaging. Journal of Imaging, 10(4), 77. https://doi.org/10.3390/jimaging10040077

Hssayeni, M., Croock, M., Salman, A., Al-khafaji, H., Yahya, Z., & Ghoraani, B. (2020). Computed tomography images for intracranial hemorrhage detection and segmentation. Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model. Data, 5(1), 14. https://doi.org/10.13026/w8q8-ky94

Hu, P., Yan, T., Xiao, B., Shu, H., Sheng, Y., Wu, Y., Shu, L., Lv, S., Ye, M., & Gong, Y. (2024). Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: A systematic review and meta-analysis. International Journal of Surgery, 110(6), 3839–3847. https://doi.org/10.1097/JS9.0000000000001266

Ibrahim, W. R., & Mahmood, M. R. (2023). Classified covid-19 by densenet121-based deep transfer learning from ct-scan images. Science Journal of University of Zakho, 11(4), 571-580. https://doi.org/10.25271/sjuoz.2023.11.4.1166

Khozama, S., & Mayya, A. M. (2022). A new range-based breast cancer prediction model using the Bayes’ theorem and ensemble learning. Information Technology and Control, 51(4), 757–770. https://doi.org/10.5755/j01.itc.51.4.31347

Kothala, L. P., & Guntur, S. R. (2024). An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection. International Journal of Imaging Systems and Technology, 34(1), e22958. https://doi.org/https://doi.org/10.1002/ima.22958

Lafraxo, S., El Ansari, M., & Koutti, L. (2024). Computer-aided system for bleeding detection in wce images based on CNN-GRU network. Multimedia Tools and Applications, 83(7), 21081–21106. https://doi.org/https://doi.org/10.1007/s11042-023-16305-w

Liu, X., & Aldrich, C. (2024). Multivariate image processing in minerals engineering with vision transformers. Minerals Engineering, 208, 108599. https://doi.org/10.1016/j.mineng.2024.108599

Majeed, M. A. A., Alrawi, A. T., & Okashi, O. M. Al. (2024). Survey on machine and deep learning methods used in CT scan brain diseases diagnosis. AIP Conference Proceedings, 3009(1). https://doi.org/https://doi.org/10.1063/5.0190368

Malik, P., Dureja, A., Dureja, A., Rathore, R. S., & Malhotra, N. (2024). Enhancing intracranial hemorrhage diagnosis through deep learning models. Procedia Computer Science, 235, 1664–1673. https://doi.org/10.1016/j.procs.2024.04.157

Murad, S. H., Awlla, A. H., & Moahmmed, B. T. (2023). Prediction lung cancer based critical factors using machine learning. Science Journal of University of Zakho, 11(3), 447–452. https://doi.org/10.25271/sjuoz.2023.11.3.1105

Neethi, A. S., Kannath, S. K., Kumar, A. A., Mathew, J., & Rajan, J. (2024). A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection. Engineering Applications of Artificial Intelligence, 133, 108192. https://doi.org/https://doi.org/10.1016/j.engappai.2024.108192

Prasher, S., Nelson, L., & Arumugam, D. (2024). Sequential CNN model for hemorrhage prediction using brain CT images. 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), 1–4. https://doi.org/https://doi.org/10.1109/AMATHE61652.2024.10582253

Rather, M. A., Khan, A., Javed, H., Jahan, S., Tabassum, R., & Begum, R. (2024). Neuropathology of neurological disorders. In Mechanism and Genetic Susceptibility of Neurological Disorders (pp. 1–33). Singapore: Springer Nature Singapore. https://doi.org/https://doi.org/10.1007/978-981-99-9404-5_1

Rivoir, D., Funke, I., & Speidel, S. (2024). On the pitfalls of Batch Normalization for end-to-end video learning: A study on surgical workflow analysis. Medical Image Analysis, 94, 103126. https://doi.org/10.1016/j.media.2024.103126

Schiariti, V., Shierk, A., Stashinko, E. E., Sukal‐Moulton, T., Feldman, R. S., Aman, C., Mendoza‐Puccini, M. C., Brandenburg, J. E., & Committee, N. I. of N. D. and S. C. P. C. D. E. O. (2024). Cerebral palsy pain instruments: Recommended tools for clinical research studies by the National Institute of Neurological Disorders and Stroke Cerebral Palsy Common Data Elements project. Developmental Medicine & Child Neurology, 66(5), 610–622. https://doi.org/https://doi.org/10.1111/dmcn.15743

Sheikh, A. M., Hossain, S., & Tabassum, S. (2024). Advances in stem cell therapy for stroke: mechanisms, challenges, and future directions. Regenerative Medicine Reports, 10–4103. https://doi.org/0.4103/RMR.REGENMED-D-23-00002

Siddiq Hassan, D. (2013). The effect of feature selection methods on machine learning model performance: A comparative study for breast cancer prediction. Science Journal of University of Zakho, 13(1), 101–112. https://doi.org/10.25271/sjuoz.2024.12.3.1429

Sindhura, C., Al Fahim, M., Yalavarthy, P. K., & Gorthi, S. (2024). Fully automated sinogram‐based deep learning model for detection and classification of intracranial hemorrhage. Medical Physics, 51(3), 1944–1956. https://doi.org/https://doi.org/10.1002/mp.16714

Studer, M., & Thompson, C. R. (2024). Prevention practice for neurological conditions. In Prevention Practice and Health Promotion (pp. 241–265). Routledge. https://doi.org/https://doi.org/10.4324/9781003525882

Suryadi, B. (2024). Methods for detecting early symptoms of stroke: A literature review. Jurnal Ilmiah Ilmu Keperawatan Indonesia, 14(01), 32–43. https://doi.org/https://doi.org/10.33221/jiiki.v14i01.3165

Szabó, S., Holb, I. J., Abriha-Molnár, V. É., Szatmári, G., Singh, S. K., & Abriha, D. (2024). Classification assessment tool: A program to measure the uncertainty of classification models in terms of class-level metrics. Applied Soft Computing, 155, 111468. https://doi.org/10.1016/j.asoc.2024.111468

Tin, T. A., Aye, M. M., Khin, E. E., Oo, T., Tun, H. M., & Pradhan, D. (2024). Performance optimization of brain tumor detection and classification based MRI by using batch normalization algorithms in deep convolution neural network. Journal of Novel Engineering Science and Technology, 3(03), 66–72. https://doi.org/10.56741/jnest.v3i03.567

Zhang, R., Ding, R., Wang, Q., Zhang, L., Fan, X., Guo, F., Chen, X., Jiang, C., Cao, J., & Wang, J. (2024). Inflammation in intracerebral hemorrhage: A bibliometric perspective. Brain Hemorrhages, 5(3), 107–116. https://doi.org/https://doi.org/10.1016/j.hest.2024.01.003

Downloads

Published

2025-07-02

How to Cite

Abd Al-Nabi, delveen. (2025). A NOVEL VITBILSTM DEEP LEARNING FRAMEWORK FOR BRAIN HEMORRHAGE PREDICTION USING CT BRAIN IMAGES. Science Journal of University of Zakho, 13(3), 321–333. https://doi.org/10.25271/sjuoz.2025.13.3.1488

Issue

Section

Science Journal of University of Zakho