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References

1 
National Cancer Information Center, 2019, Cancer incidence statistics, 2016Google Search
2 
R. Miyaki, S. Yoshida, S. Tanaka, Y. Kominami, Y. Sanomura, T. Matsuo, et al., 2015, A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer, Journal of Clinical Gastroenterology, Vol. 49, pp. 108-115DOI
3 
B. Li, M. Q.-H. Meng, 2012, Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection, IEEE Transactions on Information Technology in Biomedicine, Vol. 16, pp. 323-329DOI
4 
J. Liu, X. Yuan, 2009, Obscure bleeding detection in endoscopy images using support vector machines, Optimization and Engineering, Vol. 10, pp. 289-299DOI
5 
Y. Cong, S. Wang, J. Liu, J. Cao, Y. Yang, J. Luo, 2015, Deep sparse feature selection for computer aided endoscopy diagnosis, Pattern Recognition, Vol. 48, pp. 907-917DOI
6 
E.-K. Lim, G.-H. Kim, K.- B. Kim, 2005, Analysis System of Endoscopic Image of Early Gastric Cancer, Proceedings of KFIS Spring Conference, Vol. 15, pp. 255-260DOI
7 
K. Van De Sande, T. Gevers, C. Snoek, 2009, Evaluating color descriptors for object and scene recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, pp. 1582-1596DOI
8 
S.-E. Lee, 2003, In order not to miss early gastric cancer, Korean Society of Gastronintestinal Endoscopy Seminar, pp. 129-136Google Search
9 
T. Ojala, M. Pietikäinen, T. Mäenpää, 2002, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 971-987DOI
10 
P. Mohanaiah, P. Sathyanarayana, L. GuruKumar, 2013, Image texture feature extraction using GLCM approach, International journal of scientific and research publications, Vol. 3, pp. 1Google Search
11 
N. Sánchez-Maroño, A. Alonso-Betanzos, M. Tombilla- Sanromán, 2007, Filter methods for feature selection-a comparative study, in International Conference on Intelligent Data Engineering and Automated Learning, pp. 178-187DOI
12 
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, 2001, Feature selection for SVMs, in Advances in Neural Information Processing Systems, pp. 668-674Google Search
13 
S. Maldonado, R. Weber, 2009, A wrapper method for feature selection using support vector machines, Information Sciences, Vol. 179, pp. 2208-2217DOI
14 
T. Mehmood, K. H. Liland, L. Snipen, S. Sæbø, 2012, A review of variable selection methods in partial least squares regression, Chemometrics and Intelligent Laboratory Systems, Vol. 118, pp. 62-69DOI
15 
E. E. Osuna, 1998, Support vector machines: Training and applications, Massachusetts Institute of TechnologyGoogle Search