Ensemble learning for enhanced brain tumor diagnosis: A new approach for early detection

Abstract

Brain tumors represent one of the most extreme and complex types of cancer, requiring unique analysis for powerful remedy and management. Accurate and early identification of brain tumors can greatly enhance patient outcomes and decrease mortality. Nowadays, deep learning aids the medical field a lot by diagnosing magnetic resonance imaging images in brain tumors. The potential of deep learning architectures to improve brain tumor diagnosis accuracy was explored in this work. This study evaluated three different convolutional neural network architectures: AlexNet, VGG16, and ResNet18 as an ensemble model. By leveraging the complementary strengths of these models and applying them to a dataset sourced from local hospitals and public repositories, this research aims to address the challenges in accurate and early brain tumor detection. Our ensemble technique achieved excessive accuracy, demonstrating its potential for reliable computer-aided diagnosis (CAD) in medical imaging. However, while the results indicate an improvement in class overall performance, the novelty of this approach is restrained because it builds upon existing methodologies as opposed to offering a completely new framework. The gathered dataset was used to train and test the models. To enhance the dataset’s balance and the models’ performance, data were collected from Rizgary Hospital (Erbil) and Hiwa Hospital (Slemani), addressing the underrepresentation of cases from the Kurdistan Region of Iraq (KRI). These image enhancement techniques were applied to two categories: normal and abnormal brain tumors. Several brain tumor datasets are available online for the development of CADs, but not KRI cases, which pose challenges in their classification through deep learning models. This study was implemented with Python programming language. Out of the three models, ResNet had the highest accuracy of 98.66%, VGG16 had an accuracy of 97.8%, and AlexNet had an accuracy rate of 97.666%. The ensemble, using both majority voting and weighting voting strategies, achieved an accuracy of 98.33%.

Keywords

Brain Tumor / Computer-aided Diagnosis / Magnetic Resonance Imaging / Transfer Learning

References

[1] Abdullah, F., Jamil, A., Alazawi, E.M., & Hameed, A.A. (2024). Exploring deep learning-based approaches for brain tumor diagnosis from MRI images. In: 2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings. https://doi.org/10.1109/ICMI60790.2024.10585851

[2] Abdusalomov, A.B., Mukhiddinov, M., & Whangbo, T.K. (2023). Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers (Basel), 15(16), 4172. https://doi.org/10.3390/cancers15164172

[3] Al-Hadidi, M.R., AlSaaidah, B., & Al-Gawagzeh, M.Y. (2020). Glioblastomas brain tumour segmentation based on convolutional neural networks. International Journal of Electrical and Computer Engineering, 10(5), 4738-4744. https://doi.org/10.11591/ijece.v10i5.pp4738-4744

[4] Albalawi, E., Thakur, A., Dorai, D.R., Khan, S.B., Mahesh, T.R.,Almusharraf, A., et al. (2024). Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach. Frontiers in Computational Neuroscience, 18, 1-19. https://doi.org/10.3389/fncom.2024.1418546

[5] Alfonse, A., & Salem, A.B. (2016). An automatic classification of brain tumors through MRI using support vector machine. Egyptian Computer Science Journal, 40(3), 1110-2586

[6] Arumugam, M., Thiyagarajan, A., Adhi, L., & Alagar, S. (2024). Crossover smell agent optimized multilayer perceptron for precise brain tumor classification on MRI images. Expert Systems with Applications, 238, 121453. https://doi.org/10.1016/j.eswa.2023.121453

[7] Asif, S., Yi, W., Ain, Q.U., Hou, J., Yi, T., & Si, J. (2022). Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access, 10, 34716-34730. https://doi.org/10.1109/ACCESS.2022.3153306

[8] Bakr Siddiaue, M.A., Sakib, S., Rahman Khan, M.M., Tanzeem, A.K., Chowdhury, M., & Yasmin, N. (2020). Deep Convolutional Neural Networks Model-Based Brain Tumor Detection in Brain MRI Images. In: Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, p909-914. https://doi.org/10.1109/I-SMAC49090.2020.9243461

[9] Bansal, M., Kumar, M., Sachdeva, M., & Mittal, A. (2023). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3609-3620. https://doi.org/10.1007/s12652-021-03488-z

[10] Dhiman, A., & Satpute, P.B.S. (2019). Brain tumor segmentation in MRI images. International Journal of Research in Advent Technology, 7(9), 10-14. https://doi.org/10.32622/ijrat.78201916

[11] El Kader, I. A., Xu, G., Shuai, Z., & Saminu, S. (2021) Brain tumor detection and classification by hybrid CNN-DWA model using MR images. Current Medical Imaging, 17(10), 1248-1255. https://doi.org/10.2174/1573405617666210224113315

[12] Fang, L., & Wang, X. (2022). Brain tumor segmentation based on the dual-path network of multi-modal MRI images. Pattern Recognition, 124, 108434. https://doi.org/10.1016/j.patcog.2021.108434

[13] Gao, P., Shan, W., Guo, Y., Wang, Y., Sun, R., Cai, J., et al. (2022). Development and validation of a deep learning model for brain tumor diagnosis and classification using magnetic resonance imaging. JAMA Network Open, 5(8), e2225608. https://doi.org/10.1001/jamanetworkopen.2022.25608

[14] Ghaffari, M., Sowmya, A., & Oliver, R. (2020). Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the BraTS 2012-2018 Challenges. IEEE Reviews in Biomedical Engineering, 13, 156-168. https://doi.org/10.1109/RBME.2019.2946868

[15] Gül. M., & Kaya,Y. (2024). Comparing of brain tumor diagnosis with developed local binary patterns methods. Neural Computing and Applications, 36(13), 7545-7558. https://doi.org/10.1007/s00521-024-09476-6

[16] Harnod, T., Lin, C.L., Sung, F.C., & Kao, C.H. (2014). An association between benzodiazepine use and occurrence of benign brain tumors. Journal of the Neurological Sciences, 336(1-2), 8-12. https://doi.org/10.1016/j.jns.2013.11.009

[17] Jader, R.F., Kareem, S.W., & Awla, H.Q. (2024). Ensemble deep learning technique for detecting MRI brain tumor. Applied Computational Intelligence and Soft Computing, 2024, 6615468. https://doi.org/10.1155/2024/6615468

[18] Khairandish, M.O., Sharma, M., Jain, V., Chatterjee, J.M., & Jhanjhi, N.Z. (2022). A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. Irbm, 43(4), 290-299. https://doi.org/10.1016/j.irbm.2021.06.003

[19] Khan, A., Ghafoor, U., & Bhutta, M.R. (2022). A new deep hybrid boosted and ensemble learning-based brain tumor analysis using MRI. Sensors, 22(7), 2726.

[20] Krishnapriya, S., & Karuna, Y. (2023). Pre-trained deep learning models for brain MRI image classification. Frontiers in Human Neuroscience, 17, 1150120. https://doi.org/10.3389/fnhum.2023.1150120

[21] Kumar, A., & Ma, Y. (2024). Localization and Classification of Brain Tumor Using Multi-Layer Perceptron. Vol. 1132. Singapore:Springer Nature. https://doi.org/10.1007/978-981-99-8853-2_6

[22] Mahmud, M.I., Mamun, M., & Abdelgawad, A. (2023). A deep analysis of brain tumor detection from MR images using deep learning networks. Algorithms, 16(4), 1-19. https://doi.org/10.3390/a16040176

[23] Mathivanan, S.K., Sonaimuthu, S., Murugesan, S., Rajadurai, H., Shivahare, B.D., & Shah, M.A. (2024). Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports, 14(1), 1-15. https://doi.org/10.1038/s41598-024-57970-7

[24] Mandle, A. K., Sahu, S. P., & Gupta, G. P. (2024). WSSOA: Whale social spider optimization algorithm for brain tumor classification using deep learning technique. International Journal of Information Technology, 16(1), 1-17. https://doi.org/10.1007/s41870-024-01782-5

[25] Mostafa, A.M., Zakariah, M., & Aldakheel, E.A. (2023). Brain tumor segmentation using deep learning on MRI images. Diagnostics, 13(9), 1-22. https://doi.org/10.3390/diagnostics13091562

[26] Nawaz, S.A., Khan, D.M., & Qadri, S. (2022). Brain tumor classification based on hybrid optimized multi-features analysis using magnetic resonance imaging dataset. Applied Artificial Intelligence, 36(1), 2031824. https://doi.org/10.1080/08839514.2022.2031824

[27] Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020). A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access, 8, 55135-55144. https://doi.org/10.1109/access.2020.2978629

[28] Lauko, A., Lo, A., Ahluwalia, M.S., & Lathia, J.D. (2022). Cancer cell heterogeneity and plasticity in glioblastoma and brain tumors. Seminars in Cancer Biology, 82, 162-175. https://doi.org/10.1016/j.semcancer.2021.02.014

[29] Leena, B., & Jayanthi, A. (2020). Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification. International Journal of Imaging Systems and Technology, 30(4), 874-898. https://doi.org/10.1002/ima.22420

[30] Pacal, I., (2024). A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images. International Journal of Machine Learning and Cybernetics., 15(9), 3579-3597. https://doi.org/10.1007/s13042-024-02110-w

[31] Praveen, G. B., & Agrawal, A. (2016). Hybrid approach for brain tumor detection and classification in magnetic resonance images. In: 2015 Communication, Control and Intelligent Systems, CCIS. IEEE, United States, p162-166. https:///doi.org/10.1109/ccintels.2015.7437900

[32] Putzu, L., Piras, L., & Giacinto, G. (2020). Convolutional neural networks for relevance feedback in content based image retrieval: A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback. Multimedia Tools and Applications, 79(37-38), 26995-27021. https://doi.org/10.1007/s11042-020-09292-9

[33] Ramtekkar, P.K., Pandey, A., & Pawar, M.K. (2023). Accurate detection of brain tumor using optimized feature selection based on deep learning techniques. Multimedia Tools and Applications, 82(29), 1-31. https://doi.org/10.1007/s11042-023-15239-7

[34] Ramzan, F., Khan, M.U.G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., et al. (2020). A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. Journal of Medical Systems, 44(2), 37. https://doi.org/10.1007/s10916-019-1475-2

[35] Ranjbarzadeh, R., Kasgari, A.B., Ghoushchi, S.J., Anari, S., Naseri, M., & Bendechache, M. (2021). Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Scientific Reports, 11(1), 1-17. https://doi.org/10.1038/s41598-021-90428-8

[36] Rasheed, Z., Ma, Y.K., Ullah, I., Al Shloul, T., Tufail, A.B., Ghadi, Y.Y., et al. (2023). Automated classification of brain tumors from magnetic resonance imaging using deep learning. Brain Science, 13(4), 602. https://doi.org/10.3390/brainsci13040602

[37] Remzan, N., Tahiry, K., & Farchi, A. (2024). Advancing brain tumor classification accuracy through deep learning: harnessing radimagenet pre-trained convolutional neural networks, ensemble learning, and machine learning classifiers on MRI brain images. Multimedia Tools and Applications, 83, 0123456789. https://doi.org/10.1007/s11042-024-18780-1

[38] Roopa, Y.M., Kumar, G.N., Harsha, Y.V.S., & Aditya, P.S. (2022). Detection of Brain Tumor Types Using Deep Learning. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS. Vol. 6, p459-465. https://doi.org/10.1109/ICAIS53314.2022.9742731

[39] Santoso, I.B., Supriyono, & Utama, S.N. (2024). Multi-model of convolutional neural networks for brain tumor classification in magnetic resonance imaging images. International Journal of Intelligent Engineering and Systems, 17(5), 741-758. https://doi.org/10.22266/ijies2024.1031.56

[40] Siva Raja, P. M., & Rani, A.V. (2020). Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybernetics and Biomedical Engineering, 40(1), 440-453. https://doi.org/10.1016/j.bbe.2020.01.006

[41] Sulistyowati, T., Purwanto, P., Alzami, F., & Pramunendar, R.A. (2023). VGG16 deep learning architecture using imbalance data methods for the detection of apple leaf diseases. Moneter: Jurnal Keuangan Dan Perbankan, 11(1), 41-53. https://doi.org/10.32832/moneter.v11i1.57

[42] Wang, J., He, L., & Zhou, X. (2024). Optimizing Inception-V3 for Brain Tumor Classification Using Hybrid Precision Training and Cosine Annealing Learning Rate. In: 2024 7th International Conference on Advanced Algorithms and Control Engineering, ICAACE, p528-532. https://doi.org/10.1109/ICAACE61206.2024.10548577

[43] ZainEldin, H., Gamel, S.A., El-Kenawy, E.M., Alharbi, A.H., Khafaga, D.S., Ibrahim, A., Talaat, F.M. (2023). Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering, 10(1), 1-19. https://doi.org/10.3390/bioengineering10010018