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Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network
https://doi.org/10.22146/ijeis.34713 Wijang Widhiarso(1*), Yohannes Yohannes(2), Cendy Prakarsah(3)
(1) STMIK Global Informatika MDP Palembang
(2) STMIK Global Informtika MDP PAlembang
(3) STMIK Global Informatika MDP Palembang
(*) Corresponding Author
Abstract
Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.
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[1] D. Priyawati, I. Soesanti, and I. Hidayah, “Kajian Pustaka Metode Segmentasi Citra Pada MRI Tumor Otak,” Pros. SNST ke-6, Yogyakarta, 2016.
[2] I. W. S. E. Putra, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, 2016.
[3] T. D. Vu, H.-J. Yang, V. Q. Nguyen, A.-R. Oh, and M.-S. Kim, “Multimodal learning using convolution neural network and Sparse Autoencoder,” Chonnam Natl. Univ., 2017.
[4] C. Y. Aydo˘gdu, E. Albay, and G. Ünal, “Classification of brain tissues as lesion or healthy by 3D convolutional neural networks,” Istanbul Tek. Üniversitesi, 2017.
[5] P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. N. L. Benders, and I. Iˇsgum, “Automatic Segmentation of MR Brain Images With a Convolutional Neural Network,” IEEE Trans. Med. Imaging, 2016.
[6] S. S. M. Salehi, D. Erdogmus, and A. Gholipour, “Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging,” Harvard Med. Sch., 2017.
[7] J. Kleesiek et al., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping,” Elsevier B.V, 2016.
[8] S. Pereira, A. Oliviera, V. Alves, and C. A. Silva, “On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: A preliminary study,” Univ. Minho, 2017.
[9] S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” IEEE Trans. Med. Imaging, 2016.
[10] R. Lang, L. Zhao, and K. Jia, “Brain tumor image segmentation based on convolution neural network,” Beijing Univ. Technol., 2016.
[11] M. N. Khasanah, A. Harjoko, and I. Candradewi, “Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN),” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 6, no. 2, p. 151, 2016.
[12] X. Huang, X. Liu, and L. Zhang, “A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation,” Remote Sens, 2014.
[13] Clauditta, Lovidianti, D. Alamsyah, and Yohannes, “Menghitung Jumlah Orang dengan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM),” STMIK GI MDP Palembang, 2016.
[14] S. Goswami and L. K. P. Bhaiya, “A hybrid neuro-fuzzy approach for brain abnormality detection using GLCM based feature extraction,” Rungta Coll. Eng. Technol., 2013.
[15] A. Chaddada, P. O. Zinnb, and R. R. Colena, “Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM,” Univ. Texas, 2015.
[16] A. M. Hasan and F. Meziane, “Automated screening of MRI brain scanning using grey level statistics,” Elsevier Ltd, 2016.
[17] J. Cheng, “Brain Tumor Dataset,” Figshare, 2017.
DOI: https://doi.org/10.22146/ijeis.34713
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