Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine
Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%.
N. Gharaibeh and A. A. Kheshman, “Automated Detection of Alzheimer Disease Using Region Growing technique and Artificial Neural Network,” vol. 7, no. 5, pp. 204–208, 2013.
D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, “Multimodal classification of Alzheimer’s disease and mild cognitive impairment,” Neuroimage, vol. 55, no. 3, pp. 856–867, 2011.
M. R. Pokorny et al., “Prospective study of diagnostic accuracy comparing prostate cancer detection by transrectal ultrasound-guided biopsy versus magnetic resonance (MR) imaging with subsequent mr-guided biopsy in men without previous prostate biopsies,” Eur. Urol., vol. 66, no. 1, pp. 22–29, 2014.
E. Michael and S. Vinitski, “BONE MARROW FINDINGS ON MAGNETIC RESONANCE IMAGES OF THE KNEE : ACCENTUATION BY FAT SUPPRESSION,” vol. 8, pp. 27–31, 1990.
N. Elaiza, A. Khalid, N. Mohamed, and N. Ariff, “Fuzzy C-Means ( FCM ) for Optic Cup and Disc Segmentation with Morphological Operation,” Procedia - Procedia Comput. Sci., vol. 42, pp. 255–262, 2014.
S. Kumar, J. Kanta, D. Kumar, and M. Nasipuri, “Conditional spatial Fuzzy C-Means clustering algorithm for segmentation of MRI images,” Appl. Soft Comput. J., vol. 34, pp. 758–769, 2015.
S. R. Kannan, S. Ramathilagam, R. Devi, and A. Sathya, “Expert Systems with Applications Robust kernel FCM in segmentation of breast medical images,” Expert Syst. Appl., vol. 38, no. 4, pp. 4382–4389, 2011.
X. Wang and J. Bu, “A fast and robust image segmentation using FCM with spatial,” Digit. Signal Process., vol. 20, no. 4, pp. 1173–1182, 2010.
K. Manivannan, P. Aggarwal, V. Devabhaktuni, A. Kumar, D. Nims, and P. Bhattacharya, “Particulate matter characterization by Gray Level Co-Occurrence Matrix based support vector machines,” J. Hazard. Mater., vol. 223–224, pp. 94–103, 2012.
G. Y. Peng Yang, “Author ’ s Accepted Manuscript Reference : To appear in : Neurocomputing,” Neurocomputing, 2016.
P. M. Arabi, G. Joshi, and N. V. Deepa, “Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis,” Perspect. Sci., 2016.
Ş. Öztürk and B. Akdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 40–46, 2018.
S. P. Wang and Y. D. Cai, “Identification of the functional alteration signatures across different cancer types with Support Vector Machine and feature analysis,” Biochim. Biophys. Acta - Mol. Basis Dis., vol. 1864, no. 6, pp. 2218–2227, 2018.
C. S. Lo and C. M. Wang, “Support Vector Machine for breast MR image classification,” Comput. Math. with Appl., vol. 64, no. 5, pp. 1153–1162, 2012.
M. C. J. Christ, “Fuzzy C-Means Algorithm for Medical Image Segmentation,” no. 1, pp. 33–36, 2011.
S. E. Embough, Digital Image Processing and Analysis Aplicaton with MATLAB and CVIP Third Edition. Boca Raton: CRC Press, 2017.
Y. Q. Shi and B. Jeon, Digital Watermarking. Korea: Springer, 2006.
A. Kadir, Teori dan Aplikasi Pengolahan Citra. Yogyakarta: Andi, 2013.
P. Mohanaiah, P. Sathyanarayana, and L. GuruKumar, “image Texture Fearure Etraction Using GLCM Approach,” IJSRP, vol. 3, no. 5, 2013.
N. Zulpe and V. Pawar, “GLCM Texture Features for Brain Tumor Classification,” IJCSI, vol. 9 No. 3. 1, 2012.
Jiawei Han and Micheline Kamber, Data Mining, Second. San Francisco: Morgan Kaufmann Publisher, 2006.
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