A Transfer Learning-Based Model for Brain Tumor Detection in MRI Images

Main Article Content

Faiz Rofi Hencya
Satria Mandala
Tong Boon Tang
Mohd Soperi Mohd Zahid


brain tumor, MRI, transfer learning, Xception, prototype


Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.


J. C. Gore, “Artificial intelligence in medical imaging,” Magn. Reson. Imaging, vol. 68, pp. A1–A4, 2020, doi: 10.1016/j.mri.2019.12.006.

N. Ullah et al., “An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning,” Appl. Sci., vol. 12, no. 11, 2022, doi: 10.3390/app12115645.

N. Kesav and M. G. Jibukumar, “Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6229–6242, 2022, doi: 10.1016/j.jksuci.2021.05.008.

S. Ahmad and P. K. Choudhury, “On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images,” IEEE Access, vol. 10, no. Ml, pp. 59099–59114, 2022, doi: 10.1109/ACCESS.2022.3179376.

S. Amiri, M. Ali Mahjoub, and I. Rekik, “Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation,” Neurocomputing, vol. 313, pp. 135–142, 2018, doi: 10.1016/j.neucom.2018.05.112.

S. A. Abdelaziz Ismael, A. Mohammed, and H. Hefny, “An enhanced deep learning approach for brain cancer MRI images classification using residual networks,” Artif. Intell. Med., vol. 102, p. 101779, 2020, doi: 10.1016/j.artmed.2019.101779.

M. I. Sharif, J. P. Li, M. A. Khan, and M. A. Saleem, “Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images,” Pattern Recognit. Lett., vol. 129, pp. 181–189, 2020, doi: 10.1016/j.patrec.2019.11.019.

S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi, and J. Si, “Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images,” IEEE Access, vol. 10, pp. 34716–34730, 2022, doi: 10.1109/ACCESS.2022.3153306.

M. Malathi and P. Sinthia, “Brain tumour segmentation using convolutional neural network with tensor flow,” Asian Pacific J. Cancer Prev., vol. 20, no. 7, pp. 2095–2101, 2019, doi: 10.31557/APJCP.2019.20.7.2095.

A. A. Malibari et al., “Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface,” J. Healthc. Eng., vol. 2022, no. Mi, 2022, doi: 10.1155/2022/3987494.

J. Amin, M. A. Anjum, M. Sharif, S. Jabeen, S. Kadry, and P. Moreno Ger, “A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/3236305.

K. Muhammad, S. Khan, J. Del Ser, and V. H. C. D. Albuquerque, “Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey,” IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 2, pp. 507–522, 2021, doi: 10.1109/TNNLS.2020.2995800.

Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Comput. Med. Imaging Graph., vol. 75, pp. 34–46, 2019, doi: 10.1016/j.compmedimag.2019.05.001.

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, doi: 10.1016/j.patrec.2017.10.036.

Ö. Polat and C. Güngen, “Classification of brain tumors from MR images using deep transfer learning,” J. Supercomput., vol. 77, no. 7, pp. 7236–7252, 2021, doi: 10.1007/s11227-020-03572-9.

P. M. Siva Raja and A. V. rani, “Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 440–453, 2020, doi: 10.1016/j.bbe.2020.01.006.

M. Ahmed Hamza et al., “Optimal and Efficient Deep Learning Model for Brain Tumor Magnetic Resonance Imaging Classification and Analysis,” Appl. Sci., vol. 12, no. 15, 2022, doi: 10.3390/app12157953.

M. Toğaçar, B. Ergen, and Z. Cömert, “BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model,” Med. Hypotheses, vol. 134, p. 109531, 2020, doi: 10.1016/j.mehy.2019.109531.

J. Amin, M. Sharif, M. Yasmin, T. Saba, and M. Raza, “Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions,” Multimed. Tools Appl., vol. 79, no. 15–16, pp. 10955–10973, 2020, doi: 10.1007/s11042-019-7324-y.

J. Amin et al., “Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning,” J. Med. Syst., vol. 44, no. 2, 2020, doi: 10.1007/s10916-019-1483-2.

F. J. P. Montalbo, “A computer-aided diagnosis of brain tumors using a fine-tuned yolo-based model with transfer learning,” KSII Trans. Internet Inf. Syst., vol. 14, no. 12, pp. 4816–4834, 2020, doi: 10.3837/tiis.2020.12.011.

P. K. Chahal, S. Pandey, and S. Goel, “A survey on brain tumor detection techniques for MR images,” Multimed. Tools Appl., vol. 79, no. 29–30, pp. 21771–21814, 2020, doi: 10.1007/s11042-020-08898-3.

K. N. Deeksha, M. Deeksha, A. V. Girish, A. S. Bhat, and H. Lakshmi, “Classification of Brain Tumor and its types using Convolutional Neural Network,” 2020 IEEE Int. Conf. Innov. Technol. INOCON 2020, pp. 1–6, 2020, doi: 10.1109/INOCON50539.2020.9298306.

V. Kasala and L. Baton Rouge, “DETECTION OF TYPES OF BRAIN TUMORS USING SCRATCHED,” no. May, 2021.

S. M. Kulkarni and G. Sundari, “A framework for brain tumor segmentation and classification using deep learning algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, pp. 374–382, 2020, doi: 10.14569/IJACSA.2020.0110848.

G. Habib and S. Qureshi, “Biomedical Image Classification using CNN by Exploiting Deep Domain Transfer Learning,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1075–1083, 2021, doi: 10.12785/ijcds/100197.

B. V. Isunuri and J. Kakarla, “Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network,” Concurr. Comput. Pract. Exp., vol. 34, no. 1, pp. 1–9, 2022, doi: 10.1002/cpe.6541.

D. Tree and K. Neighbor, “Multi-Modal Case Study on MRI Brain Tumor,” vol. 20, no. 3, pp. 107–117, 2021, doi: https://doi.org/10.53799/ajse.v20i3.175.

F. Chollet, “Fatigue Behavior of Stainless Steel Sheet Specimens at Extremely High Temperatures,” SAE Int. J. Mater. Manuf., vol. 7, no. 3, pp. 560–566, 2014, doi: 10.4271/2014-01-0975.

M. NICKPARVAR, “Brain tumor mri dataset,” 2021. [Online]. Available: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. [Accessed: 11-May-2023].

O. Özkaraca et al., “Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images,” Life, vol. 13, no. 2, 2023, doi: 10.3390/life13020349.

K. Sartaj, B. and Ankita, K. and Prajakta, B. and Sameer, D. and Swati, “Brain Tumor Classification (MRI),” 2020. [Online]. Available: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri. [Accessed: 11-May-2023].

M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation,” AAAI Work. - Tech. Rep., vol. WS-06-06, pp. 24–29, 2006, doi: 10.1007/11941439_114.

D. Berrar, “Cross-validation,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, no. January 2018, pp. 542–545, 2018, doi: 10.1016/B978-0-12-809633-8.20349-X.

I. S. Dwira Kurnia Larasati, “Implementation of Template Matching on Detection of Stop Line Violations.” doi: https://doi.org/10.25077/jnte.v10n3.898.2021.

E. R. J. Geminiesty Lathifasari Djavendra, Siti Aisyah, “Desain Sistem Pengatur Lampu Lalu Lintas dengan Identifikasi Kepadatan Kendaraan Menggunakan Metode Subtraction.” doi: https://doi.org/10.25077/jnte.v7n2.541.2018.

A. M. Simundic, “Measures of Diagnostic Accuracy: Basic Definitions,” Ejifcc, vol. 19, no. 4, pp. 203–211, 2009.