HYBRID LEARNING STRATEGY COMBINING MODEL-LEVEL TRANSFER LEARNING AND DATA-LEVEL AUGMENTATION FOR BRAIN CANCER CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v11i1.6736Keywords:
brain cancer classification, , data augmentation, InceptionV3, Mixup, transfer learningAbstract
Due to the complexity of images, size, and balance of data, brain cancer diagnosis is still one of the most challenging problems to solve. It is shown that traditional classification methods based on 'first principles' do not produce ideal results, often due to different brain tumours. This research uses a hybrid model that leverages transfer learning with data augmentation and AI refinement to categorise three brain tumours: glioma, meningioma, and others. This research aims to improve the classification performance of brain cancer detection using this model. The methodology uses a framework created with a specific dataset, mixed data enhancement, and InceptionV3 model refinement to improve performance. With a validation accuracy of 0.95, the F1 scores for glioma, meningioma, and other brain tumours were 0.98, 0.95, and 0.92, respectively. This hybrid model achieves accuracy without complexity in design while addressing data scarcity and balance issues. The primary focus of this research was to create an effective and robust model for classifying brain cancers that is easy to use in low-resource clinical environments. The results demonstrate how deep learning can improve diagnostic precision and provide a scalable method for detecting brain cancer in the early stages of medical imaging
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U. P. Srivastava, ‘A Comparative Study of Deep Learning Algorithms in Classifying Brain Cancer’, in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jul. 2023, pp. 1–6. doi: 10.1109/ICCCNT56998.2023.10306832.
C. A. Ancy and M. L. Pai, ‘Brain Tumour Three-Class Classification on MRI Scans Using Transfer Learning and Data Augmentation’, in Computational Vision and Bio-Inspired Computing, S. Smys, J. M. R. S. Tavares, R. Bestak, and F. Shi, Eds., Singapore: Springer, 2021, pp. 41–56. doi: 10.1007/978-981-33-6862-0_4.
A. Rohini et al., ‘Multimodal hybrid convolutional neural network based brain tumor grade classification’, BMC Bioinformatics, vol. 24, no. 1, p. 382, Oct. 2023, doi: 10.1186/s12859-023-05518-3.
S. Akça, F. Atban, Z. Garip, and E. Ekinci, ‘XAI in the Hybrid Classification of Brain MRI Tumor Images’, in Explainable Artificial Intelligence for Biomedical Applications, River Publishers, 2023.
I. Ahmed, H. R. Nabil, G. R. Abir, T. Hossain, A. Das, and M. F. Mridha, ‘NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis’, in 2024 International Conference on Decision Aid Sciences and Applications (DASA), Dec. 2024, pp. 1–5. doi: 10.1109/DASA63652.2024.10836274.
R. D. R. Hardjodipuro, M. A. Sugiyarto, F. K. Wigati, A. A. S. Gunawan, and K. E. Setiawan, ‘Brain Cancer Classification based on T1-Weighted Images Using Deep Learning’, in 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), Feb. 2024, pp. 1–5. doi: 10.1109/AIMS61812.2024.10513273.
M. Cesarelli, F. Mercaldo, and A. Santone, ‘Explainable Deep Learning for Brain Cancer Detection and Localisation’, in 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Oct. 2023, pp. 496–501. doi: 10.1109/MetroXRAINE58569.2023.10405689.
R. Prasad, Kumar Saxena ,Amit, and S. and Laha, ‘Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique’, Cancer Investigation, vol. 43, no. 1, pp. 80–102, Jan. 2025, doi: 10.1080/07357907.2024.2431829.
N. Wang et al., ‘Deep learning-based optical coherence tomography image analysis of human brain cancer’, Biomed. Opt. Express, BOE, vol. 14, no. 1, pp. 81–88, Jan. 2023, doi: 10.1364/BOE.477311.
N. Sharma, U. S, and A. K. Moharana, ‘Improving Diagnostics for Brain Cancer: An Application of Deep Learning’, in 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Mar. 2024, pp. 1–7. doi: 10.1109/AIMLA59606.2024.10531337.
K. Lamba, S. Rani, M. Anand, and L. P. Maguluri, ‘An integrated deep learning and supervised learning approach for early detection of brain tumor using magnetic resonance imaging’, Healthcare Analytics, vol. 5, p. 100336, Jun. 2024, doi: 10.1016/j.health.2024.100336.
M. Agarwal, R. Rohan, C. Nikhil, M. Yathish, and K. Mohith, ‘Classification of Brain Tumour Disease with Transfer Learning Using Modified Pre-trained Deep Convolutional Neural Network’, in Data Science and Applications, S. J. Nanda, R. P. Yadav, A. H. Gandomi, and M. Saraswat, Eds., Singapore: Springer Nature, 2024, pp. 485–498. doi: 10.1007/978-981-99-7817-5_36.
M. Angurala, ‘Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques’, International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 5s, Art. no. 5s, Jun. 2023, doi: 10.17762/ijritcc.v11i5s.7130.
J. Shaikh and K. Shaikh, ‘Brain Tumor Classification using Transfer Learning and Ensemble Approach’, Journal of Soft Computing Paradigm, vol. 6, no. 3, pp. 284–298, Aug. 2024.
H. Gao, Y. Wan, H. Xu, L. Chen, J. Xiao, and Q. Ran, ‘SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images’, IEEE Transactions on Consumer Electronics, pp. 1–1, 2025, doi: 10.1109/TCE.2025.3527061.
K. Rattan, G. Bathla, and V. Wasson, ‘Advancements in Brain Tumor Detection Using Hybrid Deep Learning Models’, in 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), Nov. 2024, pp. 1383–1389. doi: 10.1109/ICTACS62700.2024.10841263.
Y. Mohana Ropa, K. R. Moon, and T. Singh, ‘Deep Learning-Based Hybrid Framework Utilizing OpenCV and CNN for Automated Brain Tumor Detection and MRI Image Classification’, in 2024 4th International Conference on Sustainable Expert Systems (ICSES), Oct. 2024, pp. 1674–1682. doi: 10.1109/ICSES63445.2024.10762991.
A. A. S. Ali, ‘Brain Tumor Classification Using A Hybrid Deep Learning Model: Leveraging Densenet121 And Inceptionv2 Architectures’, Electronic Journal of University of Aden for Basic and Applied Sciences, vol. 5, no. 4, pp. 455–463, Dec. 2024, doi: 10.47372/ejua-ba.2024.4.402.
H. Byeon, ‘Advanced Brain Tumor Classification Using an EfficientNet and Autoencoder Hybrid Model: A Comprehensive MRI Analysis’, Nanotechnology Perceptions, pp. 1336–1349, Oct. 2024, doi: 10.62441/nano-ntp.vi.2639.
A. Dutta, T. Batabyal, M. Basu, and S. T. Acton, ‘An efficient convolutional neural network for coronary heart disease prediction’, Expert Systems with Applications, vol. 159, p. 113408, Nov. 2020, doi: 10.1016/j.eswa.2020.113408.
E. M. G. Younis, M. N. Mahmoud, A. M. Albarrak, and I. A. V, ‘A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images’, Sep. 21, 2024, Preprints: 2024091627. doi: 10.20944/preprints202409.1627.v1.
S. Shanjida, Md. S. Islam, and M. Mohiuddin, ‘Hybrid model-based Brain Tumor Detection and Classification using Deep CNN-SVM’, in 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), May 2024, pp. 1467–1472. doi: 10.1109/ICEEICT62016.2024.10534376.
B. B. Vimala, S. Srinivasan, S. K. Mathivanan, Mahalakshmi, P. Jayagopal, and G. T. Dalu, ‘Detection and classification of brain tumor using hybrid deep learning models’, Sci Rep, vol. 13, no. 1, p. 23029, Dec. 2023, doi: 10.1038/s41598-023-50505-6.
A. Biswas and M. S. Islam, ‘A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification’, Journal of Information Systems Engineering and Business Intelligence, vol. 9, no. 1, Art. no. 1, Apr. 2023, doi: 10.20473/jisebi.9.1.1-15.
J. Dixon, O. Akinniyi, A. Abdelhamid, G. A. Saleh, M. M. Rahman, and F. Khalifa, ‘A Hybrid Learning-Architecture for Improved Brain Tumor Recognition’, Algorithms, vol. 17, no. 6, Art. no. 6, Jun. 2024, doi: 10.3390/a17060221.
‘Brain Cancer - MRI dataset’. Accessed: May 09, 2025. [Online]. Available: https://www.kaggle.com/datasets/orvile/brain-cancer-mri-dataset
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Copyright (c) 2025 Budiman Budiman, Nur Alamsyah, Venia Restreva Danestiara, Muhamad Achya Arifudin, Dede Irman Pirdaus
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