Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies

Özlem Akboyraz; Soner Kızıloluk

doi:10.29132/ijpas.1922851

Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies

Abstract

Diagnosing brain tumors accurately at an early stage plays an important role in treatment planning and patient survival. Since manual interpretation of medical images is time-consuming and depends heavily on expert experience, automated methods have become increasingly valuable. This study evaluates five transfer learning-based convolutional neural network models, namely DenseNet121, EfficientNetB0, InceptionV3, MobileNetV2, and ResNet50, for multi-class brain tumor classification. To improve classification performance, several data augmentation techniques, including brightness and contrast adjustment, blurring, flipping, and rotation, were applied together with a differential learning rate strategy. This strategy allowed the lower layers of the pretrained networks to preserve general visual representations, while the upper layers adapted more effectively to tumor-specific patterns. The results show that this training approach improved all evaluation metrics compared with fixed learning rate training. EfficientNetB0 achieved the highest performance, with 98.78% accuracy and an F1-score of 0.9878.

Keywords

Ethical Statement

Authors declares that this study complies with research and publication ethic.

Thanks

The authors would like to thank the reviewers and editorial boards of the International Journal of Pure and Applied Sciences.

References

J. Amin, M. Sharif, M. Raza, T. Saba, R. Sial, and S. A. Shad, “Brain tumor detection: a long short-term memory (LSTM)-based learning model,” Neural Comput & Applic, vol. 32, no. 20, pp. 15965–15973, Oct. 2020, doi: 10.1007/s00521-019-04650-7.
Y. Fan et al., “Burden and trends of brain and central nervous system cancer from 1990 to 2019 at the global, regional, and country levels,” Arch Public Health, vol. 80, no. 1, p. 209, Sep. 2022, doi: 10.1186/s13690-022-00965-5.
R. Disci, F. Gurcan, and A. Soylu, “Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models,” Cancers, vol. 17, no. 1, p. 121, Jan. 2025, doi: 10.3390/cancers17010121.
A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging,” Cancers, vol. 15, no. 16, p. 4172, Aug. 2023, doi: 10.3390/cancers15164172.
A. B. Naeem, B. Senapati, and A. Zaidi, “Enhancing Brain Tumor Detection from MRI-Based Images Through Deep Transfer Learning Models,” AI, vol. 6, no. 12, p. 305, Nov. 2025, doi: 10.3390/ai6120305.
A. Biswas and Md. S. Islam, “A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification,” J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 1–15, Apr. 2023, doi: 10.20473/jisebi.9.1.1-15.
O. Özkaraca et al., “Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images,” Life, vol. 13, no. 2, p. 349, Jan. 2023, doi: 10.3390/life13020349.
S. Srinivasan, D. Francis, S. K. Mathivanan, H. Rajadurai, B. D. Shivahare, and M. A. Shah, “A hybrid deep CNN model for brain tumor image multi-classification,” BMC Med Imaging, vol. 24, no. 1, p. 21, Jan. 2024, doi: 10.1186/s12880-024-01195-7.

Md. A. Talukder et al., “An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning,” Expert Systems with Applications, vol. 230, p. 120534, Nov. 2023, doi: 10.1016/j.eswa.2023.120534.
M. Ait Amou, K. Xia, S. Kamhi, and M. Mouhafid, “A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization,” Healthcare, vol. 10, no. 3, p. 494, Mar. 2022, doi: 10.3390/healthcare10030494.
N. Asaad Zebari, A. A. H. Alkurdi, R. B. Marqas, and M. Shamal Salih, “Enhancing Brain Tumor Classification with Data Augmentation and DenseNet121,” ACAD J NAWROZ UNIV, vol. 12, no. 4, pp. 323–334, Oct. 2023, doi: 10.25007/ajnu.v12n4a1985.
S. U. Rehman Khan, S. Asif, M. Zhao, W. Zou, Y. Li, and C. Xiao, “ShallowMRI: A novel lightweight CNN with novel attention mechanism for Multi brain tumor classification in MRI images,” Biomedical Signal Processing and Control, vol. 111, p. 108425, Jan. 2026, doi: 10.1016/j.bspc.2025.108425.
S. U. R. Khan, S. Asif, O. Bilal, and H. U. Rehman, “Lead-cnn: lightweight enhanced dimension reduction convolutional neural network for brain tumor classification,” Int. J. Mach. Learn. & Cyber., vol. 16, no. 9, pp. 6627–6646, Sep. 2025, doi: 10.1007/s13042-025-02637-6.
S. Suryawanshi and S. B. Patil, “Efficient Brain Tumor Classification with a Hybrid CNN-SVM Approach in MRI,” JAIT, vol. 15, no. 3, pp. 340–354, 2024, doi: 10.12720/jait.15.3.340-354.
T. Semwal, S. Jain, A. Mohanta, and A. Jain, “A hybrid CNN-SVM model optimized with PSO for accurate and non-invasive brain tumor classification,” Neural Comput & Applic, vol. 37, no. 33, pp. 27901–27930, Nov. 2025, doi: 10.1007/s00521-025-11078-9.
Nickparvar, M., “Brain Tumor MRI Dataset. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset.” 2021.
G. R. Vuppari, N. Gupta, A. El-Sayed, and X. Xiong, “Wildfire Detection Using Vision Transformer with the Wildfire Dataset,” 2025, arXiv. doi: 10.48550/ARXIV.2505.17395.
Z. Wang et al., “A Comprehensive Survey on Data Augmentation,” 2024, arXiv. doi: 10.48550/ARXIV.2405.09591.
T. Islam, Md. S. Hafiz, J. R. Jim, Md. M. Kabir, and M. F. Mridha, “A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions,” Healthcare Analytics, vol. 5, p. 100340, Jun. 2024, doi: 10.1016/j.health.2024.100340.
A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan, “Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review,” J. Imaging, vol. 9, no. 4, p. 81, Apr. 2023, doi: 10.3390/jimaging9040081.
F. Garcea, A. Serra, F. Lamberti, and L. Morra, “Data augmentation for medical imaging: A systematic literature review,” Computers in Biology and Medicine, vol. 152, p. 106391, Jan. 2023, doi: 10.1016/j.compbiomed.2022.106391.
L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017, arXiv. doi: 10.48550/ARXIV.1712.04621.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, p. 60, Dec. 2019, doi: 10.1186/s40537-019-0197-0.
E. Goceri, “Medical image data augmentation: techniques, comparisons and interpretations,” Artif Intell Rev, vol. 56, no. 11, pp. 12561–12605, Nov. 2023, doi: 10.1007/s10462-023-10453-z.
J. Ma, C. Hu, P. Zhou, F. Jin, X. Wang, and H. Huang, “Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation,” Applied Sciences, vol. 13, no. 11, p. 6478, May 2023, doi: 10.3390/app13116478.
X. Liu, K. Ono, and R. Bise, “A data augmentation approach that ensures the reliability of foregrounds in medical image segmentation,” Image and Vision Computing, vol. 147, p. 105056, Jul. 2024, doi: 10.1016/j.imavis.2024.105056.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 2261–2269. doi: 10.1109/CVPR.2017.243.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA: IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018, doi: 10.48550/ARXIV.1801.04381.
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 2019, doi: 10.48550/ARXIV.1905.11946.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” 2015, arXiv. doi: 10.48550/ARXIV.1512.00567.
D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” 2020, doi: 10.48550/ARXIV.2010.16061.

Details

Primary Language

English

Subjects

Image Processing

Journal Section

Research Article

Authors

Özlem Akboyraz
0009-0007-8960-9769
Türkiye

Soner Kızıloluk ^*
0000-0002-0381-9631
Türkiye

Publication Date

June 22, 2026

Submission Date

April 3, 2026

Acceptance Date

May 27, 2026

Published in Issue

Year 2026 Volume: 12 Number: 1

DOI

https://doi.org/10.29132/ijpas.1922851

IZ

https://izlik.org/JA72AB22TP

Cite

RIS / Bibtex

APA

Akboyraz, Ö., & Kızıloluk, S. (2026). Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences, 12(1), 352-370. https://doi.org/10.29132/ijpas.1922851

AMA

1.Akboyraz Ö, Kızıloluk S. Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences. 2026;12(1):352-370. doi:10.29132/ijpas.1922851

Chicago

Akboyraz, Özlem, and Soner Kızıloluk. 2026. “Improving CNN-Based Brain Tumor Classification via Differential Learning Rate and Data Augmentation Strategies”. International Journal of Pure and Applied Sciences 12 (1): 352-70. https://doi.org/10.29132/ijpas.1922851.

EndNote

Akboyraz Ö, Kızıloluk S (June 1, 2026) Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences 12 1 352–370.

IEEE

[1]Ö. Akboyraz and S. Kızıloluk, “Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies”, International Journal of Pure and Applied Sciences, vol. 12, no. 1, pp. 352–370, June 2026, doi: 10.29132/ijpas.1922851.

ISNAD

Akboyraz, Özlem - Kızıloluk, Soner. “Improving CNN-Based Brain Tumor Classification via Differential Learning Rate and Data Augmentation Strategies”. International Journal of Pure and Applied Sciences 12/1 (June 1, 2026): 352-370. https://doi.org/10.29132/ijpas.1922851.

JAMA

1.Akboyraz Ö, Kızıloluk S. Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences. 2026;12:352–370.

MLA

Akboyraz, Özlem, and Soner Kızıloluk. “Improving CNN-Based Brain Tumor Classification via Differential Learning Rate and Data Augmentation Strategies”. International Journal of Pure and Applied Sciences, vol. 12, no. 1, June 2026, pp. 352-70, doi:10.29132/ijpas.1922851.

Vancouver

1.Özlem Akboyraz, Soner Kızıloluk. Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences. 2026 Jun. 1;12(1):352-70. doi:10.29132/ijpas.1922851