Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor
Keywords:Convolutional Neural Network, Brain Tumor, Data Augmentation
Detecting brain tumors is an active area of research in brain image processing. This paper proposes a methodology to segment and classify brain tumors using magnetic resonance images (MRI). Convolutional Neural Networks (CNN) are one of the effective detection methods and have been employed for tumor segmentation. We optimized the total number of layers and epochs in the model. First, we run the CNN with 1000 epochs to see its best-optimized number. Then we consider six models, increasing the number of layers from one to six. It allows seeing the overfitting according to the number of layers.
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B.Freymann, K. Farahani, and C. Davatzikos, “Advancing the cancer genomeatlas glioma mri collections with expert segmentation labels and radiomicfeatures,”Scientific data, vol. 4, no. 1, pp. 1–13, 2017.
M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A brain tumor segmentationframework based on outlier detection,”Medical image analysis, vol. 8, no. 3,pp. 275–283, 2004.
A. Jalalifar, H. Soliman, M. Ruschin, A. Sahgal, and A. Sadeghi-Naini,“A brain tumor segmentation framework based on outlier detection usingone-class support vector machine,” in2020 42nd Annual International Conerence of the IEEE Engineering in Medicine & Biology Society (EMBC).IEEE, 2020, pp. 1067–1070.
L. Harper, F. Barkhof, P. Scheltens, J. M. Schott, and N. C. Fox, “An algo-rithmic approach to structural imaging in dementia,”Journal of Neurology,Neurosurgery & Psychiatry, vol. 85, no. 6, pp. 692–698, 2014.
G. B. Frisoni, N. C. Fox, C. R. Jack, P. Scheltens, and P. M. Thompson,“The clinical use of structural mri in alzheimer disease,”Nature ReviewsNeurology, vol. 6, no. 2, pp. 67–77, 2010.
R. C. Petersen, P. Aisen, L. A. Beckett, M. Donohue, A. Gamst, D. J.Harvey, C. Jack, W. Jagust, L. Shaw, A. Togaet al., “Alzheimer’s dis-ease neuroimaging initiative (adni): clinical characterization,”Neurology,vol. 74, no. 3, pp. 201–209, 2010.
S. Qiu, P. S. Joshi, M. I. Miller, C. Xue, X. Zhou, C. Karjadi, G. H. Chang,A. S. Joshi, B. Dwyer, S. Zhuet al., “Development and validation of an in-terpretable deep learning framework for alzheimer’s disease classification,”Brain, vol. 143, no. 6, pp. 1920–1933, 2020.
C. A. Charu,Neural Networks and Deep Learning: A Textbook. Spinger,2018.
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamar ́ıa, M. A. Fadhel, M. Al-Amidie, and L. Farhan,“Review of deep learning: Concepts, cnn architectures, challenges, appli-cations, future directions,”Journal of big Data, vol. 8, no. 1, pp. 1–74,2021.
S. Afaq and S. Rao, “Significance of epochs on training a neural network,”International Journal of Scientific and Technology Research, vol. 19, no. 6,pp. 485–488, 2020.
S. Irsheidat and R. Duwairi, “Brain tumor detection using artificial convo-lutional neural networks,” in2020 11th International Conference on Infor-mation and Communication Systems (ICICS), 2020, pp. 197–203.
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Copyright (c) 2022 Destiny Rankins, Dewayne A. Dixon, Yeona Kang, Seonguk Kim
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.