A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging

Alper Özatılgan; Mahir Kaya

doi:10.35377/saucis...1518139

EN

A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging

Abstract

The brain, which controls important vital functions such as vision, hearing and movement, negatively affects our lives when it is sick. Of these diseases, the deadliest is undoubtedly the brain tumor, which can occur in all age groups and can be benign or malignant. Therefore, early diagnosis and prognosis are very important. Magnetic Resonance (MR) images are used for the detection and treatment of brain tumor types. Successful results in the detection of diseases from medical images with Convolutional Neural Networks (CNN) depend on the optimum creation of the number of layers and other hyper-parameters. In this study, we propose a CNN model that will achieve the highest accuracy with the least number of layers. A public data set consisting of 4 different classes (Meningioma, Glioma, Pituitary and Normal) obtained for use in the training of CNN models was trained and tested with 50 different deep learning models designed, and a better result was obtained when compared with the existing studies in the literature with 99.47% accuracy and 99.44% F1 score values.

Keywords

References

R. Singh, C. Prabha, S. Kumari, K. Murugan, M. R. Veeramanickam and T. Singh, “Accuracy Enhancement in Detecting Pituitary Tumors Using Deep Learning,” In 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), pp:1067-1072, IEEE, 2023
L. Thau, V. Reddy, and P. Singh, “Anatomy, central nervous system,” In StatPearls [Internet]. StatPearls Publishing, 2022
B-L. Isabelle et al, “The Global Brain Health Survey: Development of a Multi-Language Survey of Public Views on Brain Health,” Front. Public Health, Sec. Public Health Education and Promotion, Vol:8, doi: https://doi.org/10.3389/fpubh.2020.00387, 2020
J. Cahill, G. LoBiondo‐Wood, N. Bergstrom, and T. Armstrong, “Brain tumor symptoms as antecedents to uncertainty: An integrative review,” Journal of Nursing Scholarship, vol. 44, no. 2, pp:145-155, 2012
J. S. Barnholtz-Sloan, Q. T. Ostrom, D. Cote, “Epidemiology of Brain Tumors,” Neurologic Clinics, Vol. 36, Issue 3, pp:395-419, 2018
A-R. Fathi and U. Roelcke, “Meningioma,” Neuro-Oncology (Le Abrey, Section Editor) Curr Neurol Neurosci, Vol. 13, no.337, Doi:10.1007/s11910-013-0337-4, 2013
J. Wiemels, M. Wrensch and E. B. Claus, “Epidemiology and Etiology of Meningioma,” Invited Review, J Neurooncol, Vol. 99, pp:307-314, Doi: 10.1007/s11060-010-0386-3, 2010
C. Apra, M. Peyre and M. Kalamarides, “Current Treatment Options for Meningioma,” Expert Review of Neurotherapeutics, HAL Open Science, Vol. 18, no. 3, pp:241-249, 2018

A.S. Modrek, N.S. Bayin and D.G. Placantonakis, “Brain Stem Cells as the Cell of Origin in Glioma,” World J Stem Cells, Vol. 6, no. 1, pp:43-52, 2014
N. A. O. Bush, S. M. Chang and M. S. Berger, “Current and Future Strategies for Treatment of Glioma,” Neurosurg Rev, vol. 40, pp:1-14, 2017
S. D. Muhammad and Z. Kobti, “An Ensemble Deep Learning Approach for Enhanced Classification of Pituitary Tumors,” In 2023 IEEE Symposium Series on Computational Intelligence, IEEE, p: 427-432, 2023
A. M. Gab Allah, A. M. Sarhan and N. M. Elshennawy, “Classification of brain MRI tumor images based on deep learning PGGAN Augmentation,” Diagnostics, Vol. 11, no. 12, 2021
M. K. Abd-Ellah, A. I. Awad, A. A. Khalaf and H. F. Hamed, “A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned,” Magnetic resonance imaging, Vol. 61, pp: 300-318, 2019
S. A. Yazdan, R. Ahmad, N. Iqbal, A. Rizwan, A. N. Khan, and D. H. Kim, "An efficient multi-scale convolutional neural network based multi-class brain MRI classification for SaMD," Tomography, Vol. 8, no. 4, pp:1905-1927, 2022
M. R. Ismael and I. Abdel-Qader, “Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network,” 2018 IEEE Uluslararası Elektro/Bilgi Teknolojisi Konferansı, 2018.
A. Pashaei, H. Sajedi and N. Jazayeri, “Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines,” ICCKE2018, Ferdowsi University of Mashhad, pp: 314-319
S. Deepak, P.M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Computers in Biology and Medicine, ELSEVIER, 2019
Z. N. K. Swati, Q. Zhao, M. Kabir, F. Ali, S. Ahmed and J. Lu, “Brain Tumor Classification for MR Images Using Transfer Learning and Fine-Tuning,” Computerized Medical Imaging and Graphics, ELSEVIER, 2019
H. H. Sultan, N. M. Salem and W. Al-Atabany, “Multi-classification of brain tumor images using deep neural network,” IEEE Access, Vol. 7, pp:69215–69225, 2019
N. Ghassemi, A. Shoeibi and M. Rouhani, “Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images,” Biomedical Signal Processing and Control, Elsevier, 2020 doi: https://doi.org/10.1016/j.bspc.2019.101678
R. Hashemzehi, S. J. S. Mahdavi, M. Kheirabadi and S. R. Kamel, “Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE,” Biocybernetics And Biomedical Engineering, Elsevier, pp: 1225-1232, doi: https://doi.org/10.1016/j.bbe.2020.06.001, 2020
K. Kaplan, Y. Kaya, M. Kuncan and H. M. Ertunç, “Brain tumor classification using modified local binary patterns (LBP) feature extraction methods,” Medical Hypotheses, Elsevier, doi: https://doi.org/10.1016/j.mehy.2020.109696 , 2020
A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, “A deep learning based framework for automatic brain tumors classification using transfer learning,” Circuits, Systems, and Signal Processing, Vol. 39, no. 2, pp:757–775, doi:10.1007/S00034-019-01246-3/TABLES/8, 2020
W. Ayadi, W. Elhamzi, I. Charfı and M. Atrl, “Deep CNN for Brain Tumor Classification,” Neural Processing Letters, Springer, Vol. 53, pp:671-700, doi: https://doi.org/10.1007/s11063-020-10398-2 , 2021
E. U. Haq, H. Jianjun, K. Li, H. U. Haq and T. Zhang, “An MRI‑based deep learning approach for efficient classification of brain tumors,” Journal of Ambient Intelligence and Humanized Computing, Springer, doi: https://doi.org/10.1007/s12652-021-03535-9 , 2023
S. R. Sowrirajan, S. Balasubramanian and R. S. P. Raj, “MRI Brain Tumor Classification Using a Hybrid VGG16-NADE Model,” Article-Engineering, Technology and Techniques, BABT, Vol. 66 doi: https://doi.org/10.1590/1678-4324-2023220071 ,2022
D. R. Yerukalareddy and E. Pavlovskiy, “Brain Tumor Classification Based on MR Images Using Gan as a Pre-trained Model,” IEEE Ural-Siberian Conference On Computational Technologies in Cognitive Science, Genomics And Biomedicine (CSGB), pp:380-384, doi: 10.1109/CSGB53040.2021.9496036, 2021
H. Kibriya, M. Masood, M. Nawaz, T. Nazir, “Multiclass classification of brain tumors using a novel CNN architecture,” Multimedia Tools and Applications, SPRINGER, Vol. 81, pp:29847-29863, doi: https://doi.org/10.1007/s11042-022-12977-y ,2022
A. A. Nasiri et al, “Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images,” Computers, Materials & Continua, Tech Science Press, Vol. 73, no.3, pp: 5735-5753, doi: 10.32604/cmc.2022.03174, 2022
M. Kaya, and Y. Çetin-Kaya, “A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia,” Engineering Applications of Artificial Intelligence, Vol. 133, no. 108494, 2024
M. Kaya and Y. Çetin-Kaya, “A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level,” IEEE Access, 2024
M. Kaya, “Bayesian Optimization-based CNN Framework for Automated Detection of Brain Tumors,” Balkan Journal of Electrical and Computer Engineering, Vol. 11, no. 4, pp:395-404, 2023
Y. Çetin-Kaya and M. Kaya, “A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging,” Diagnostics, Vol. 14, no. 4, 2024
https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” arXiv: 1511.08458v2, 2015
E. Cengil and A. Çınar, “A New Approach For Image Classification: Convolutional Neural Network,” Europan Journal of Technic, INESEG, Vol 6, Num 2, pp: 96-103, 2016
E. Ö. YILMAZ and T. KAVZOĞLU, “Derin Öğrenmenin Temel Prensipleri ve Uzaktan Algılama Alanındaki Uygulamaları,” Harita Dergisi, Vol. 166, pp. 25-43, 2021
F. Özyurt, E. Sert, E. Avci, and E. Dogantekin, "Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy," Measurement, Vol. 147, no. 106830, 2019
Y. Lu, S. Yi, N. Zeng, Y. Liu and Y. Zhang, “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, Elsevier, Vol. 267, pp:378-384, 2017
R. Yamashita, M. Nishio, R. K. G. Do and K. Togashi, “Convolutional Neural Networks: An Overview and Application in Radiology,” Insights Into Imaging, Vol. 9, no. 4, pp:611-629, 2018
I. Goodfellow, Y. Bengio, A. Courville, “Deep learning,” MIT Press, 2016
W. Hao, W. Yizhou, L. Yaqin and S. Zhili, “The Role of Activation Function in CNN,” Proceedings, 2020 2nd International Conference on Information Technology and Computer Application, ITCA, pp:429-432, doi: https://doi.org/10.1109/ITCA52113.2020.00096, 2020
B. Singh, S. Patel, A. Vijavvargiya and R. Kumar, “Analyzing the Impact of Activation Functions on the Performance of the Data-Driven Gait Model,” Results in Engineering, Vol. 18, 2023
S. Sharma, S. Sharma and A. Athaiya, “Activation Functions in Neural Networks,” International Journal of Engineering Applied Sciences and Technology, Vol. 4 no. 12, pp:310-316, 2020
S. R. Dubey, S. K. Singh and B. B. Chaudhuri, “Activation functions in deep learning: A comprehensive survey and benchmark,” Neurocomputing, Vol. 503, 92-108, 2022
Bayram F., “Derin Öğrenme Tabanlı Otomatik Plaka Tanıma,” Politeknik Dergisi, Vol. 23, no. 4, pp:955-960, 2020
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, Vol. 15, no. 2014, pp:1929-1958, 2014
K. Liu, G. Kang, N. Zhang and B. Hou, "Breast cancer classification based on fully-connected layer first convolutional neural networks,", IEEE Access, Vol. 6, pp:23722-23732, 2018
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, Vol. 521, no. 7553, pp:436-444, 2015 Article Information Form

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Alper Özatılgan ^*
0009-0003-5344-5142
Türkiye

Mahir Kaya
0000-0001-9182-271X
Türkiye

Early Pub Date

December 31, 2024

Publication Date

December 31, 2024

Submission Date

July 18, 2024

Acceptance Date

December 25, 2024

Published in Issue

Year 2024 Volume: 7 Number: 3

DOI

https://doi.org/10.35377/saucis...1518139

IZ

https://izlik.org/JA53XZ42DN

Cite

RIS / Bibtex

APA

Özatılgan, A., & Kaya, M. (2024). A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging. Sakarya University Journal of Computer and Information Sciences, 7(3), 482-493. https://doi.org/10.35377/saucis...1518139

AMA

1.Özatılgan A, Kaya M. A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging. SAUCIS. 2024;7(3):482-493. doi:10.35377/saucis.1518139

Chicago

Özatılgan, Alper, and Mahir Kaya. 2024. “A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging”. Sakarya University Journal of Computer and Information Sciences 7 (3): 482-93. https://doi.org/10.35377/saucis. 1518139.

EndNote

Özatılgan A, Kaya M (December 1, 2024) A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging. Sakarya University Journal of Computer and Information Sciences 7 3 482–493.

IEEE

[1]A. Özatılgan and M. Kaya, “A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging”, SAUCIS, vol. 7, no. 3, pp. 482–493, Dec. 2024, doi: 10.35377/saucis...1518139.

ISNAD

Özatılgan, Alper - Kaya, Mahir. “A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging”. Sakarya University Journal of Computer and Information Sciences 7/3 (December 1, 2024): 482-493. https://doi.org/10.35377/saucis. 1518139.

JAMA

1.Özatılgan A, Kaya M. A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging. SAUCIS. 2024;7:482–493.

MLA

Özatılgan, Alper, and Mahir Kaya. “A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 3, Dec. 2024, pp. 482-93, doi:10.35377/saucis. 1518139.

Vancouver

1.Alper Özatılgan, Mahir Kaya. A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging. SAUCIS. 2024 Dec. 1;7(3):482-93. doi:10.35377/saucis. 1518139

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