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Vol. 6, No. 2, May 2021

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Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

https://doi.org/10.22219/kinetik.v6i2.1219
Agus Eko Minarno
Universitas Muhammadiyah Malang
http://orcid.org/0000-0002-4063-4812
Mochammad Hazmi Cokro Mandiri
Universitas Muhammadiyah Malang
Yuda Munarko
Universitas Muhammadiyah Malang
Hariyady Hariyady
Universitas Muhammadiyah Malang

Corresponding Author(s) : Agus Eko Minarno

aguseko@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 2, May 2021

Abstract

Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

Keywords

Brain Tumor MRI Convolutional Neural Network Hyperparameter Tuning
Minarno, A. E., Hazmi Cokro Mandiri, M. ., Munarko, Y. ., & Hariyady, H. (2021). Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(2). https://doi.org/10.22219/kinetik.v6i2.1219
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References
  1. M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, and SW Good, "Multi-grade brain tumor classification using deep CNN with extensive data augmentation," J. Comput. Sci., vol. 30, pp. 174–182, 2019. https://doi.org/10.1016/j.jocs.2018.12.003
  2. MA Khan et al., "Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection," Microsc. Res. Tech., Vol. 82, no. 6, pp. 909–922, 2019. https://doi.org/10.1002/jemt.23238
  3. A. Minz and C. Mahobiya, "MR image classification using adaboost for brain tumor type," Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, pp. 701–705, 2017. https://doi.org/10.1109/IACC.2017.0146
  4. G. Mohan and MM Subashini, "MRI based medical image analysis: Survey on brain tumor grade classification," Biomed. Signal Process. Control, vol. 39, pp. 139–161, 2018. https://doi.org/10.1016/j.bspc.2017.07.007
  5. M. Toğaçar, B. Ergen, and Z. Cömert, "Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks," Med. Biol. Eng. Comput., 2020. https://doi.org/10.1007/s11517-020-02290-x
  6. S. Deepak and PM Ameer, "Brain tumor classification using deep CNN features via transfer learning," Comput. Biol. Med., vol. 111, no. June, pp. 103345, 2019. https://doi.org/10.1016/j.compbiomed.2019.103345
  7. A. Pashaei, H. Sajedi, and N. Jazayeri, "Brain tumor classification via convolutional neural network and extreme learning machines," 2018 8th Int. Conf. Comput. Knowl. Eng. ICCKE 2018, no. Iccke, pp. 314–319, 2018. https://doi.org/10.1109/ICCKE.2018.8566571
  8. H. Shahamat and M. Saniee Abadeh, "Brain MRI analysis using a deep learning based evolutionary approach," Neural Networks, vol. 126, pp. 218–234, 2020. https://doi.org/10.1016/j.neunet.2020.03.017
  9. N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, and M. Shoaib, "A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor," IEEE Access, vol. 8, pp. 55135–55144, 2020. https://doi.org/10.1109/ACCESS.2020.2978629
  10. Z. Fan, F. Xu, X. Qi, C. Li, and L. Yao, "Classification of Alzheimer's disease based on brain MRI and machine learning," Neural Comput. Appl., vol. 32, no. 7, pp. 1927–1936, 2020. https://doi.org/10.1007/s00521-019-04495-0
  11. H. Mohsen, E.-SA El-Dahshan, E.-SM El-Horbaty, and A.-BM Salem, "Classification using deep learning neural networks for brain tumors," Futur. Comput. J. Informatics, vol. 3, no. 1, pp. 68–71, 2018. https://doi.org/10.1016/j.fcij.2017.12.001
  12. S. Kumar and DP Mankame, "Optimization driven Deep Convolution Neural Network for brain tumor classification," Biocybern. Biomed. Eng., vol. 40, no. 3, pp. 1190–1204, 2020. https://doi.org/10.1016/j.bbe.2020.05.009
  13. D. Ravi et al., "Deep Learning for Health Informatics," IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 4–21, 2017. https://doi.org/10.1109/JBHI.2016.2636665
  14. 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. https://doi.org/10.1109/ACCESS.2019.2897131
  15. X. Liu, X. Zhou, and X. Qian, "Transparency-guided ensemble convolutional neural network for the stratification between pseudoprogression and true progression of glioblastoma multiform in MRI," J. Vis. Commun. Image Represent., vol. 72, no. August, pp. 102880, 2020. https://doi.org/10.1016/j.jvcir.2020.102880
  16. P. Nagaraj, V. Muneeswaran, L. Veera Reddy, P. Upendra, and M. Vishnu Vardhan Reddy, "Programmed Multi-Classification of Brain Tumor Images Using Deep Neural Network," Proc. Int. Conf. Intell. Comput. Control Syst. ICICCS 2020, pp. 865–870, 2020. https://doi.org/10.1109/ICICCS48265.2020.9121016
  17. 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.
  18. LP Bhaiya and VK Verma, "Classification of MRI Brain Images Using Neural Networks," vol. 2, no. 5, pp. 751–756, 2012.
  19. S. Bhuvaji, A. Kadam, P. Bhumkar, and S. Dedge, “Brain Tumor Classification (MRI) | Kaggle, ”2020.
  20. M. Agarwal, SK Gupta, and KK Biswas, "Development of Efficient CNN model for Tomato crop disease identification," Sustain. Comput. Informatics Syst., vol. 28, p. 100407, 2020. https://doi.org/10.1016/j.suscom.2020.100407
  21. 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.
  22. YD Zhang, C. Pan, J. Sun, and C. Tang, "Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU," J. Comput. Sci., vol. 28, pp. 1–10, 2018. https://doi.org/10.1016/j.jocs.2018.07.003
  23. M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification," Neurocomputing, vol. 321, pp. 321–331, 2018. https://doi.org/10.1016/j.neucom.2018.09.013
  24. 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. https://doi.org/10.1109/CLOUD.2019.00097
  25. WY 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. https://doi.org/10.1016/j.ijleo.2018.07.044
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