Abstract

Early identification of brain tumors is crucial for effective therapy and improved clinical outcomes. For brain tumor detection, this study presents a novel neural network that combines a three-layer Convolutional Neural Network (CNN) with a Bidirectional LSTM (BiLSTM). The proposed approach can automatically identify complex patterns and features from medical imaging data by using advanced computational techniques, leading to faster and more accurate evaluations. This study contributes to the field of medical image analysis by providing a reliable and accurate computational approach for brain tumor recognition. The BR35H dataset is used in this research, which comprises MRI images of both tumor and non-tumor subjects. The proposed CNN-BiLSTM model showed strong performance in tumor detection, with an accuracy of 98%, a precision of 98.48%, a recall of 97.5%, and an F1-score of 97.99%. The effectiveness of the proposed method is demonstrated through a thorough final evaluation, which includes a confusion matrix, comparison with current algorithms, and experiments using different data sizes. These experimental outcomes show the significance of combining CNN and BiLSTM models for brain tumor recognition.

Keywords

Brain Tumors, Bidirectional LSTM (BiLSTM), Convolutional Neural Network (CNN), Deep Learning, Machine Learning, Magnetic Resonance Imaging (MRI),

Downloads

Download data is not yet available.

References

  1. G. Pelluet, M. Rizkallah, O. Acosta, D. Mateus, Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation. In: A. Crimi, S. Bakas (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, Springer, Cham, 12962, (2022). https://doi.org/10.1007/978-3-031-08999-2_7
  2. S.R. Chandaran, G. Muthusamy, L.R. Sevalaiappan, N. Senthilkumaran, Deep Learning-based Transfer Learning Model in Diagnosis of Diseases with Brain Magnetic Resonance Imaging. Acta Polytechnica Hungarica, 19(5), (2022) 127–147.
  3. A. Diker, A. Elen, A. Subasi, Brain stroke Detection from Computed Tomography Images using Deep Learning Algorithms. In Applications of Artificial Intelligence in Medical Imaging, Academic Press, (2023) 207–222. https://doi.org/10.1016/B978-0-443-18450-5.00013-X
  4. T.K. Behera, M.A. Khan, S. Bakshi, Brain MR image classification using superpixel-based deep transfer learning. IEEE Journal of Biomedical and Health Informatics, 28(3), (2022) 1218–1227. https://doi.org/10.1109/JBHI.2022.3216270
  5. W. Ayadi, W. Elhamzi, I. Charfi, M. Atri, Deep CNN for brain tumor classification. Neural Processing Letters, 53(1), (2021) 671–700. https://doi.org/10.1007/s11063-020-10398-2
  6. A. Rehman, M.A. Khan, T. Saba, Z. Mehmood, U. Tariq, N. Ayesha, Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microscopy Research and Technique, 84(1), (2021) 133–149. https://doi.org/10.1002/jemt.23597
  7. D.S. Kishore, Y.M.M. Babu, K. Radhika, A.K. Reddy, Fuzzy c-means based medical image retrieval for identifying most clinically relevant images. Multimedia Tools and Applications, 83(18), (2024) 55283–55303. https://doi.org/10.1007/s11042-023-17440-0
  8. T. Umamaheswari, Y.M.M. Babu, ViT-MAENB7: An Innovative Breast Cancer Diagnosis Model from 3D Mammograms using advanced Segmentation and Classification Process. Computer Methods and Programs in Biomedicine, 257, (2024) 108373. https://doi.org/10.1016/j.cmpb.2024.108373
  9. S. Asif, M. Zhao, X. Chen, Y. Zhu, BMRI-NET: A Deep Stacked Ensemble Model for Multi-Class Brain Tumor Classification from MRI Images. Interdisciplinary Sciences: Computational Life Sciences, 15(3), (2023) 499–514. https://doi.org/10.1007/s12539-023-00571-1
  10. T. Umamaheswari, Y.M. Mohanbabu, CNN-FS-IFuzzy: A New Enhanced Learning Model Enabled by Adaptive Tumor Segmentation for Breast Cancer Diagnosis using 3D Mammogram Images. Knowledge-Based Systems, 288, (2024) 111443. https://doi.org/10.1016/j.knosys.2024.111443
  11. T. Sadad, A. Rehman, A. Munir, T. Saba, U. Tariq, N. Ayesha, R. Abbasi, Brain Tumor Detection and Multi-Classification using Advanced Deep Learning Techniques. Microscopy Research and Technique, 84(6), (2021) 1296–1308. https://doi.org/10.1002/jemt.23688
  12. S.U.R. Khan, M. Zhao, S. Asif, X. Chen, Hybrid‐NET: A Fusion of DenseNet169 and Advanced Machine Learning classifiers for enhanced brain Tumor Diagnosis. International Journal of Imaging Systems and Technology, 34(1), (2024) e22975. https://doi.org/10.1002/ima.22975
  13. S.M. Vijithananda, M.L. Jayatilake, B. Hewavithana, T. Gonçalves, L.M. Rato, B.S. Weerakoon, T.D. Kalupahana, A.D. Silva, K.D. Dissanayake, Feature extraction from MRI ADC Images for Brain Tumor Classification using Machine Learning Techniques. Biomedical Engineering Online, 21(1), (2022) 52. https://doi.org/10.1186/s12938-022-01022-6
  14. R.W. Anwar, M. Abrar, F. Ullah, (2023) Transfer Learning in Brain Tumor Classification: Challenges, Opportunities, and Future Prospects. In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), IEEE, Jeju Island, Korea. https://doi.org/10.1109/ICTC58733.2023.10392830
  15. F. Ullah, M. Nadeem, M. Abrar, F. Amin, A. Salam, A. Alabrah, H. AlSalman, Evolutionary Model for Brain Cancer-Grading and Classification. IEEE Access, 11, (2023) 126182–126194. https://doi.org/10.1109/ACCESS.2023.3330919
  16. K. Demir, B. Arı, F. Demir, Detection of Brain Tumor with a Pre-Trained Deep Learning Model based on Feature Selection using MR Images. Firat University Journal of Experimental and Computational Engineering, 2(1), (2023) 23–31. https://doi.org/10.5505/fujece.2023.36844
  17. H.H. Sultan, N.M. Salem, W. Al-Atabany, Multi-classification of Brain Tumor images using Deep Neural Network. IEEE Access, 7, (2019) 69215–69225. https://doi.org/10.1109/ACCESS.2019.2919122
  18. S. Deepak, P.M. Ameer, Brain Tumor Categorization from Imbalanced MRI dataset using Weighted Loss and Deep Feature Fusion. Neurocomputing, 520, (2023) 94–102. https://doi.org/10.1016/j.neucom.2022.11.039
  19. H. Sadr, M. Nazari, S. Yousefzadeh-Chabok, H. Emami, R. Rabiei, A. Ashraf, Enhancing Brain Tumor classification in MRI Images: A deep Learning-Based Approach for Accurate Diagnosis. Image and Vision Computing, 159, (2025) 105555. https://doi.org/10.1016/j.imavis.2025.105555
  20. G. Eleyan, Z. Al-Barakeh, R. Ghandour, B. Neji, A. Eleyan, (2025) Brain Tumor Detection Via Ensemble CNN-Based Deep Learning Models. In 2025 6th International Conference on Bio-engineering for Smart Technologies (BioSMART), IEEE, Paris, France. https://doi.org/10.1109/BioSMART66413.2025.11046093
  21. S.K. Chhotray, D. Mishra, S.P. Pati, S. Mishra, An Optimized Cascaded CNN approach for Feature Extraction from brain MRIs for Tumor Classification. IEEE Access, 13, (2025) 32681–32705.https://doi.org/10.1109/ACCESS.2025.3543214
  22. M. Hammad, M. ElAffendi, A.A. Ateya, A.A. Abd El-Latif, Efficient brain tumor detection with lightweight end-to-end deep learning model. Cancers, 15(10), (2023) 2837. https://doi.org/10.3390/cancers15102837
  23. F. Ullah, M. Nadeem, M. Abrar, F. Amin, A. Salam, S. Khan, Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with advanced Data Privacy and Security Measures. Mathematics, 11(19), (2023) 4189. https://doi.org/10.3390/math11194189
  24. F. Ullah, M. Nadeem, M. Abrar, M. Al-Razgan, T. Alfakih, F. Amin, A. Salam, Brain Tumor Segmentation from MRI images using Handcrafted Convolutional Neural Network. Diagnostics, 13(16), (2023) 2650. https://doi.org/10.3390/diagnostics13162650
  25. F. Ullah, M. Nadeem, M. Abrar, Revolutionizing Brain Tumor Segmentation in MRI with Dynamic Fusion of Handcrafted Features and Global Pathway-based Deep Learning. KSII Transactions on Internet & Information Systems, 18(1), (2024). http://doi.org/10.3837/tiis.2024.01.007
  26. R. Hashemzehi, S.J.S. Mahdavi, M. Kheirabadi, S.R. Kamel, Detection of Brain Tumors from MRI Images based on Deep Learning using hybrid Model CNN and NADE. Biocybernetics and Biomedical Engineering, 40(3), (2020) 1225–1232. https://doi.org/10.1016/j.bbe.2020.06.001
  27. R. Biswas, S. Roy, A. Biswas, MRI and CT image Indexing and Retrieval using Steerable Pyramid Transform and Local Neighborhood difference Pattern. International Journal of Computers and Applications, 44(11), (2022) 1005–1014. https://doi.org/10.1080/1206212X.2022.2092937
  28. R. Singh, A. Goel, D.K. Raghuvanshi, MR brain Tumor Classification Employing ICA and Kernel-based Support Vector Machine. Signal, Image and Video Processing, 15(3), (2021) 501–510. https://doi.org/10.1007/s11760-020-01770-9
  29. O.A.M.F. Alnaggar, B.N. Jagadale, S.H. Narayan, M.A.N. Saif, Brain Tumor Detection from 3D MRI using Hyper-Layer Convolutional Neural Networks and Hyper-Heuristic Extreme Learning Machine. Concurrency and Computation: Practice and Experience, 34(24), (2022) e7215. https://doi.org/10.1002/cpe.7215
  30. A.A. Dehkordi, M. Hashemi, M. Neshat, S. Mirjalili, A.S. Sadiq, (2022). Brain Tumor Detection and Classification using a New Evolutionary Convolutional Neural Network. arXiv preprint arXiv:2204.12297. https://doi.org/10.48550/arXiv.2204.12297
  31. S. Lu, S.H. Wang, Y.D. Zhang, Detection of Abnormal Brain in MRI via Improved AlexNet and ELM Optimized by Chaotic Bat Algorithm. Neural Computing and Applications, 33(17), (2021) 10799–10811. https://doi.org/10.1007/s00521-020-05082-4
  32. M.Y.B. Murthy, A. Koteswararao, M.S. Babu, Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis. Biomedical Engineering Letters, 12(1), (2022) 37–58. https://doi.org/10.1007/s13534-021-00209-5
  33. M. Toğaçar, B. Ergen, Z. Cömert, BrainMRNet: Brain Tumor Detection using Magnetic Resonance Images with a Novel Convolutional Neural Network Model. Medical Hypotheses, 134, (2020) 109531. https://doi.org/10.1016/j.mehy.2019.109531
  34. N. Cinar, M. Kaya, B. Kaya, A Novel Convolutional neural network-Based Approach for Brain Tumor Classification using Magnetic Resonance Images. International Journal of Imaging Systems and Technology, 33(3), (2023) 895–908. https://doi.org/10.1002/ima.22839
  35. V.K. Waghmare, M.H. Kolekar, Brain Tumor Classification Using Deep Learning. In: C. Chakraborty, A. Banerjee, M. Kolekar, L. Garg, B. Chakraborty (eds), Internet of Things for Healthcare Technologies. Studies in Big Data, Springer, 73, (2021) 155–175. https://doi.org/10.1007/978-981-15-4112-4_8
  36. H. Mzoughi, I. Njeh, A. Wali, M.B. Slima, A. BenHamida, C. Mhiri, K.B. Mahfoudhe, Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. Journal of Digital Imaging, 33(4), (2020) 903–915. https://doi.org/10.1007/s10278-020-00347-9
  37. B.B. Gupta, A. Gaurav, V. Arya, Deep CNN based brain tumor detection in intelligent systems. International Journal of Intelligent Networks, 5, (2024) 30–37. https://doi.org/10.1016/j.ijin.2023.12.001
  38. M.I. Nazir, A. Akter, M.A.H. Wadud, M.A. Uddin, Utilizing customized CNN for brain tumor prediction with explainable AI. Heliyon, 10(20), (2024) e38997. https://doi.org/10.1016/j.heliyon.2024.e38997
  39. N. Rasool, N.A. Wani, J.I. Bhat, S. Saharan, V.K. Sharma, B.S. Alsulami, H. Alsharif, M.D. Lytras, CNN-TumorNet: Leveraging explainability in deep learning for precise brain tumor diagnosis on MRI images. Frontiers in Oncology, 15, (2025) 1554559. https://doi.org/10.3389/fonc.2025.1554559
  40. H. Zhu, L. Wang, N. Shen, Y. Wu, S. Feng, Y. Xu, C. Chen, W. Chen, MS-HNN: Multi-scale hierarchical neural network with squeeze and excitation block for neonatal sleep staging using a single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, (2023) 2195–2204. https://doi.org/10.1109/TNSRE.2023.3266876
  41. A. Hamada, (2020) Br35H: Brain tumor detection 2020. Kaggle Dataset. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection