Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet

  • Muhammad Aji Purnama Wibowo Universitas Muhammadiyah Malang
  • Muhammad Bima Al Fayyadl Universitas Muhammadiyah Malang
  • Yufis Azhar Universitas Muhammadiyah Malang
  • Zamah Sari Universitas Muhammadiyah Malang
Keywords: brain tumor, EfficientNet, Convolutional Neural Network, MRI


A brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%.



Download data is not yet available.


Hariyady, “Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, 2021,

A. Yentür, “Brain MRI Image Classification using kernel SVM,” no. June, 2021

K. Pattabiraman, S. K. Muchnik, and N. Sestan, “The evolution of the human brain and disease susceptibility,” Curr. Opin. Genet. Dev., vol. 65, pp. 91–97, 2020,

Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, and Q. Sun, “Deep learning for image-based cancer detection and diagnosis − A survey,” Pattern Recognit., vol. 83, pp. 134–149, 2018,

I. B. L. M. Suta, R. S. Hartati, and Y. Divayana, “Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging),” Maj. Ilm. Teknol. Elektro, vol. 18, no. 2, 2019, DOI:

H. A. Khan, W. Jue, M. Mushtaq, and M. U. Mushtaq, “Brain tumor classification in MRI image using convolutional neural network,” Math. Biosci. Eng., vol. 17, no. 5, pp. 6203–6216, 2020, doi: 10.3934/MBE.2020328.

W. H.-P. SeNTIK and undefined 2021, “Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak,” Ejournal.Jak-Stik.Ac.Id, vol. 5, no. 1, 2021, [Online]. Available:

A. S. Febrianti, T. A. Sardjono, and A. F. Babgei, “Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine,” J. Tek. ITS, vol. 9, no. 1, 2020, doi: 10.12962/j23373539.v9i1.51587.

M. O. Khairandish, M. Sharma, V. Jain, J. M. Chatterjee, and N. Z. Jhanjhi, “A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images,” Irbm, vol. 1, pp. 1–10, 2021, doi: 10.1016/j.irbm.2021.06.003.

J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, “A distinctive approach in brain tumor detection and classification using MRI,” Pattern Recognit. Lett., vol. 139, pp. 118–127, 2020,

M. Mittal, L. M. Goyal, S. Kaur, I. Kaur, A. Verma, and D. Jude Hemanth, “Deep learning based enhanced tumor segmentation approach for MR brain images,” Appl. Soft Comput. J., vol. 78, pp. 346–354, 2019,

N. Abiwinanda, M. Hanif, ST Hesaputra, A. Handayani, and TR Mengko, Brain Tumor Classification Using Convolutional Neural Network, vol. 68, no. 1. Springer Singapore, 2018.

Umaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). ”A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor Classification”. IEEE Access, 1– 1. doi:10.1109/access.2019.2904145.

A. Yang, X. Yang, W. Wu, H. Liu, and Y. Zhuansun, “Research on feature extraction of tumor image based on convolutional neural network,” IEEE Access, vol. 7, pp. 24204–24213, 2019, doi: 10.1109/ACCESS.2019.2897131.

D. R. Nayak, N. Padhy, P. K. Mallick, M. Zymbler, and S. Kumar, “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, 2022, doi: 10.3390/axioms11010034.

S. Bhuvaji, A. Kadam, P. Bhumkar, and S. Dedge, “Brain Tumor Classification (MRI) | Kaggle, ”2020.

X. Xiao, M. Yan, S. Basodi, C. Ji, and Y. Pan, "Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm," arXiv, 2020.

MP Ranjit, G. Ganapathy, K. Sridhar, and V. Arumugham, "Efficient deep learning hyperparameter tuning using cloud infrastructure: Intelligent distributed hyperparameter tuning with Bayesian optimization in the cloud," IEEE Int. Conf. Cloud Comput. CLOUD, vol. 2019-July, pp. 520–522,2019.

Y Lee, SM Park, and KB Sim, "Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm," Optics (Stuttg)., vol. 172, no. July, pp. 359–367, 2018.

M. Koklu, I. Cinar, and Y. S. Taspinar, “Classification of rice varieties with deep learning methods,” Comput. Electron. Agric., vol. 187, no. June, p. 106285, 2021, doi: 10.1016/j.compag.2021.106285.

Annisa Fitria Nurjannah, Andi Shafira Dyah Kurniasari, Zamah Sari, and Yufis Azhar, “Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 2, pp. 330–338, 2022, doi: 10.29207/resti.v6i2.4001.

Ulfah Nur Oktaviana and Yufis Azhar, “Klasifikasi Sampah Menggunakan Ensemble DenseNet169,” Resti, vol. 1, no. 1, pp. 19–25, 2021, doi:

How to Cite
Purnama Wibowo, M. A., Muhammad Bima Al Fayyadl, Yufis Azhar, & Zamah Sari. (2022). Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 538 - 547.
Artikel Teknologi Informasi