MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods
DOI:
https://doi.org/10.30871/jaic.v9i6.10363Keywords:
Brain Tumor, Feature Extraction, Machine Learning, MRI ClassificationAbstract
Brain tumor classification using MRI images plays a crucial role in modern medical diagnostics, offering fast and accurate support for disease detection. This study proposes a classification approach that combines feature extraction using EfficientNet B0 with conventional machine learning algorithms. MRI brain images are preprocessed and resized to match EfficientNet B0 input dimensions. Feature vectors are extracted and subsequently processed using PCA for dimensionality reduction and SMOTE for class balancing. The resulting data are classified using various machine learning algorithms including Support Vector Machine, XGBoost, LightGBM, and others. Experimental results show that Support Vector Machine achieved the highest accuracy of 96%, followed by XGBoost and LightGBM at 94%. The combination of EfficientNet B0 feature extraction and lightweight classifiers proved to be effective, matching the performance of more complex deep learning models. This study does not focus on measuring computational cost directly, but rather demonstrates that combining EfficientNetB0 feature extraction with machine learning algorithms can achieve performance comparable to deep learning approaches. This highlights that lightweight models remain competitive in terms of accuracy without requiring highly complex architectures. Future work can explore this method on other medical imaging datasets and enhance model interpretability for clinical adoption.
Downloads
References
[1] M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, “Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging,” Mar. 01, 2022, MDPI. doi: 10.3390/s22051960.
[2] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition.” [Online]. Available: http://image-net.org/challenges/LSVRC/2015/
[3] M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.”
[4] J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks.” [Online]. Available: http://image-net.org/challenges/LSVRC/2017/results
[5] G. Balaji, R. Sen, and H. Kirty, “Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks,” Aug. 2022, [Online]. Available: http://arxiv.org/abs/2208.13264
[6] C. Szegedy et al., “Going Deeper with Convolutions.”
[7] I. Soesanti, H. Avizenna, and I. Ardiyanto, “Classification of Brain Tumor MRI Image using Random Forest Algorithm and Multilayers Perceptron,” 2020.
[8] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556
[9] A. GHOSH and A. KOLE, “A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification,” Oct. 26, 2021. doi: 10.36227/techrxiv.16863136.v1.
[10] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller, “Causability and explainability of artificial intelligence in medicine,” Jul. 01, 2019, Wiley-Blackwell. doi: 10.1002/widm.1312.
[11] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.” [Online]. Available: http://gradcam.cloudcv.org
[12] S. M. Lundberg, P. G. Allen, and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions.” [Online]. Available: https://github.com/slundberg/shap
[13] J. Seetha and S. S. Raja, “Brain tumor classification using Convolutional Neural Networks,” Biomedical and Pharmacology Journal, vol. 11, no. 3, pp. 1457–1461, Sep. 2018, doi: 10.13005/bpj/1511.
[14] M. Li, Y. Jiang, Y. Zhang, and H. Zhu, “Medical image analysis using deep learning algorithms,” Front Public Health, vol. 11, 2023, doi: 10.3389/fpubh.2023.1273253.
[15] P. K. Mall et al., “A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities,” Healthcare Analytics, vol. 4, Dec. 2023, doi: 10.1016/j.health.2023.100216.
[16] A. R. W. Sait and R. Nagaraj, “An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images,” Diagnostics, vol. 14, no. 2, Jan. 2024, doi: 10.3390/diagnostics14020227.
[17] I. Zine-dine, J. Riffi, K. El Fazazi, M. A. Mahraz, and H. Tairi, “Brain Tumor Classification using Machine and Transfer Learning,” Scitepress, May 2022, pp. 566–571. doi: 10.5220/0010762800003101.
[18] M. A. Purnama Wibowo, M. B. Al Fayyadl, Y. Azhar, and Z. Sari, “Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet,” Jurnal RESTI, vol. 6, no. 4, pp. 538–547, Aug. 2022, doi: 10.29207/resti.v6i4.4119.
[19] D. Candra, G. Wibisono, M. Ayu, and M. Afrad, “LEDGER: Journal Informatic and Information Technology Transfer Learning model Convolutional Neural Network menggunakan VGG-16 untuk Klasifikasi Tumor Otak pada Citra Hasil MRI,” 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Firza Findia Jiven, Rumini Rumini
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
