Improving Brain Tumor Classification Performance Using EfficientNetB0 Integrated with CBAM Attention Mechanism

Authors

  • Abd Salam At Taqwa State University of Makassar
  • Satriawan Muh. Christian University of Indonesia Paulus
  • Sheila Christian University of Indonesia Paulus
  • Puput Politeknik Nusantara of Makassar
  • Fettyana State University of Makassar
  • Ayutri Fajar University

DOI:

10.33395/sinkron.v10i2.15931

Keywords:

Brain Tumor Classification, Magnetic Resonance Imaging (MRI), EfficientNetB0, CBAM

Abstract

Accurate classification of brain tumors using magnetic resonance imaging (MRI) requires robust automated methods to support clinical diagnosis, particularly when tumor types present subtle visual distinctions. In this study, the Convolutional Block Attention Module (CBAM) is incorporated into the EfficientNetB0 architecture to improve feature representation for multi-class brain tumor classification. The performance of the proposed model is evaluated against the baseline EfficientNetB0 under identical training and testing conditions. EfficientNetB0 with CBAM achieves a training accuracy of 99.76% and a validation accuracy of 99.45%, with corresponding training and validation losses of 0.0085 and 0.0241. On an independent test dataset, the model attains a test accuracy of 99.25% and a loss of 0.0207. In comparison, the baseline EfficientNetB0 model attains a training accuracy of 52.68%, validation accuracy of 46.20%, and test accuracy of 43.32%, accompanied by significantly higher loss values. At the class level, the proposed model demonstrates macro-average precision, recall, and F1-score of 0.99, whereas the baseline model yields macro-average values of approximately 0.54 for precision and recall, and 0.50 for F1-score. Although CBAM integration increases computational time per evaluation step from 395 ms to 601 ms, the marked improvement in classification accuracy and error reduction underscores the value of attention mechanisms. These results demonstrate that attention-based feature refinement significantly enhances deep learning performance for medical image classification, particularly in multi-class brain tumor diagnosis.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Aamir, M., Rahman, Z., Dayo, Z. A., Abro, W. A., Uddin, M. I., Khan, I., Imran, A. S., Ali, Z., Ishfaq, M., Guan, Y., & Hu, Z. (2022). A deep learning approach for brain tumor classification using MRI images. Computers and Electrical Engineering, 101, 108105. https://doi.org/10.1016/j.compeleceng.2022.108105

Anand, V., Khajuria, A., Pachauri, R. K., & Gupta, V. (2026). Multi-class classification of brain tumors using optimized CNN and transfer learning techniques. Scientific Reports, 16(1), 4709. https://doi.org/10.1038/s41598-025-34806-6

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., & Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954–961. https://doi.org/10.1038/s41591-019-0447-x

Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015). Correction: Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLOS ONE, 10(12), e0144479. https://doi.org/10.1371/journal.pone.0144479

Díaz-Pernas, F. J., Martínez-Zarzuela, M., Antón-Rodríguez, M., & González-Ortega, D. (2021). A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. Healthcare, 9(2), 153. https://doi.org/10.3390/healthcare9020153

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116

Ke, L., Hu, G., Zhao, M., Liu, Z., Lv, Z., & Yang, Y. (2026). Brain tumor classification from MRI images using a multi-scale channel attention CNN integrated with SVM. Scientific Reports. https://doi.org/10.1038/s41598-026-36164-3

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

Kshatri, S. S., & Singh, D. (2023). Convolutional Neural Network in Medical Image Analysis: A Review. Archives of Computational Methods in Engineering, 30(4), 2793–2810. https://doi.org/10.1007/s11831-023-09898-w

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

Lu, N.-H., Huang, Y.-H., Liu, K.-Y., & Chen, T.-B. (2025). Deep learning-driven brain tumor classification and segmentation using non-contrast MRI. Scientific Reports, 15(1), 27831. https://doi.org/10.1038/s41598-025-13591-2

Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2025). Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information, 16(3), 195. https://doi.org/10.3390/info16030195

Prabhas, K. S., Basem, A., Lakshmi, L., Talha, A., Mohammed, S. H., Khan, M. I., & Khedher, N. Ben. (2025). A Deep learning framework for brain tumor detection using CNNs and transfer learning on MRI scans. Systems and Soft Computing, 7, 200389. https://doi.org/10.1016/j.sasc.2025.200389

Rahman, M. M. (2024). Brain Cancer - MRI dataset. Mendeley Data.

Rasheed, Z., Ma, Y.-K., Ullah, I., Al-Khasawneh, M., Almutairi, S. S., & Abohashrh, M. (2024). Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors. Bioengineering, 11(7), 701. https://doi.org/10.3390/bioengineering11070701

Rehman, A., Naz, S., Razzak, M. I., Akram, F., & Imran, M. (2020). A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Systems, and Signal Processing, 39(2), 757–775. https://doi.org/10.1007/s00034-019-01246-3

Sadr, H., Nazari, M., Yousefzadeh-Chabok, S., Emami, H., Rabiei, R., & Ashraf, A. (2025). Enhancing brain tumor classification in MRI images: A deep learning-based approach for accurate diagnosis. Image and Vision Computing, 159, 105555. https://doi.org/10.1016/j.imavis.2025.105555

Saraei, M., & Liu, S. (2023). Attention-based Deep Learning Approaches in Brain Tumor Image Analysis: A Mini Review. Frontiers in Health Informatics, 12, 164. https://doi.org/10.30699/fhi.v12i0.493

Shah, H. A., Saeed, F., Yun, S., Park, J.-H., Paul, A., & Kang, J.-M. (2022). A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet. IEEE Access, 10, 65426–65438. https://doi.org/10.1109/ACCESS.2022.3184113

Shashidhar, R., Manasa, R., Megha, K. M., Priyanga, P., Manjunath, A. S., & Roopa, M. (2024). Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification. 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON), 1–5. https://doi.org/10.1109/NMITCON62075.2024.10698924

Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In K. Chaudhuri & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 6105–6114). PMLR. https://proceedings.mlr.press/v97/tan19a.html

Wong, Y., Su, E. L. M., Yeong, C. F., Holderbaum, W., & Yang, C. (2025). Brain tumor classification using MRI images and deep learning techniques. PLOS One, 20(5), e0322624. https://doi.org/10.1371/journal.pone.0322624

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module (pp. 3–19). https://doi.org/10.1007/978-3-030-01234-2_1

Downloads


Crossmark Updates

How to Cite

At Taqwa, A. S., Muh., S., Sheila Eunike, K., Puput, K. D., Fettyana, & Ayutri, W. (2026). Improving Brain Tumor Classification Performance Using EfficientNetB0 Integrated with CBAM Attention Mechanism. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(2), 893-904. https://doi.org/10.33395/sinkron.v10i2.15931

Issue

Vol. 10 No. 2 (2026): Article Research April, 2026

Section

Articles

License

Copyright (c) 2026 Abd Salam At Taqwa, Satriawan Muh., Sheila, Puput, Fettyana, Ayutri

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC