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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Developing accurate predictive models for brain tumor growth using LSTM and GRU techniques

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pp. 2083–2093Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2299XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2299
Pages:
2083–2093

Abstract

Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, this study aims to create accurate models that can predict the growth of brain tumours. We used a complete method that included steps like picture cropping, normalization, resampling, and improvement to prepare the BraTS dataset from the University of Pennsylvania. After that, we used the U-net model to do photo segmentation, which was very important for appropriately defining the rims of the tumour. We use LSTM to predict how a tumour will develop through the years and then enhance it with a blended LSTM-GRU model to make it greater correct and use less computing electricity. We checked how properly our models anticipated the future by using looking on the dice coefficient and Intersection over Union (IOU) measures for segmentation consequences. next, we checked the accuracy of our predictions the usage of suggest Absolute mistakes (MAE) evaluation The comparison of performance showed that the blend model not only minimizes losses better, but it also makes much better predictions about how tumours will grow, which suggests that it could be used in personalized treatment plans and keeping track of progress.

Keywords

Brain tumor predictionLSTM and GRU modelsImage segmentationU-NETTumor growth analysis

Subject Classifications

68T0592B20

References

[1] M. Menagadevi, S. Sharmila, D. Thiyagarajan, N. Madian, and S. Devaraj, “Brain Tumor Detection Using VGG 16 & Efficient Net B3 Model,” in 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India,  pp. 167–172 (2024), doi: 10.1109/ICACCS60874.2024.10717050.
[2] S. S. Tuppad, V. S. Handur, and V. P. Baligar, “Brain Tumor Classification Using Deep Learning Models,” in 2024 Second International Conference on Advances in Information Technology (ICAIT), Chikkamagaluru, Karnataka, India, pp. 1–5 (2024), doi: 10.1109/ICAIT61638.2024.10690630.
[3] A. Banerjee, K. Jaiswal, T. Biswas, V. Sharma, M. Bal, and S. Mishra, “Brain Tumor Detection and Classification Using a Hyperparameter Tuned Convolutional Neural Network,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, pp. 502–506 (2023), doi: 10.1109/IC3I59117.2023.10397907.
[4] N. Dudam, L. Chato, N. Motupalli, K. T. Kashyap, M. Sane, and M. Bollina, “Enhancing Brain Tumor Identification through Convolutional Neural Networks and Transformer Models,” in 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, pp. 359–365 (2025), doi: 10.1109/CCWC62904.2025.10903559.
[5] R. Golchha, P. Khobragade, and A. Talekar, “Design of an Efficient Model for Health Status Prediction Using LSTM, Transformer, and Bayesian Neural Networks,” in 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), Nagpur, India, pp. 1–5 (2024), doi: 10.1109/ICICET59348.2024.10616353.
[6] B. Gheflati and H. Rivaz, “Vision Transformers for Classification of Breast Ultrasound Images,” in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2022).
[7] J. Chen and Y. Yuan, “Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning,” IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2025.3549292.
[8] B. S. Madan, N. J. Zade, N. P. Lanke, S. S. Pathan, S. N. Ajani, and P. Khobragade, “Self-Supervised Transformer Networks: Unlocking New Possibilities for Label-Free Data,” Panamerican Mathematical Journal, vol. 34, no. 4, pp. 194–210 (Dec. 2024). [Online]. Available: https://doi.org/10.52783/pmj.v34.i4.1878
[9] G. Kaur, M. Kaur, J. Singh, M. M. Laishram, S. Lakhanpal, and M. Kumar, “Effective strategies for writing maintainable code in cryptographic software development,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 29, no. 2-A, pp. 609–616 (2026), doi: 10.47974/JDMSC-2503.
[10] M. Kaur, M. K. Goyal, D. P. Yadav, S. M. Patil, M. Kumar, and M. E. Abdelhag, “Exploring facial biometrics in multi-factor authentication systems for secure banking applications,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 29, no. 2-A, pp. 655–662 (2026), doi: 10.47974/JDMSC-2508.
[11] P. Sahane, M. Gulhane, N. Rakesh, S. M. M. Naidu, M. Grover, and V. Mahajan, “Evaluating lightweight encryption schemes for resource-constrained devices in wireless sensor networks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1803–1812 (2025), doi: 10.47974/JDMSC-2180.

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