Asian Journal of Information Technology
Year:
2016
Volume:
15
Issue:
15
Page No.
2709 - 2715
References
Amirkhani, A., M.R. Mosavi, F. Mohammadizadeh and S.B. Shokouhi, 2014. Classification of intraductal breast lesions based on the fuzzy cognitive map. Arabian J. Sci. Eng., 39: 3723-3732.
CrossRef | Direct Link | Amirkhani, A., M.R. Mosavi, S.B. Shokouhi and F. Mohammadizadeh, 2012. A novel fuzzy cognitive map based method for the differentiation of intraductal breast lesions. Proceedings of the 5th International Conference on Biomedical Engineering and Informatics, October 16-18, 2012, Chongqing, pp: 6-11.
Ananda, R.S. and T. Thomas, 2010. Texture description of low grade and high grade glioma using statistical features in brain MRIs. Int. J. Recent Trends Eng. Technol., 4: 27-33.
Direct Link | Castellano, G., L. Bonilha, L.M. Li and F. Cendes, 2004. Texture analysis of medical images. Clin. Radiol., 59: 1061-1069.
CrossRef | Groumpos, P.P., 2010. Fuzzy Cognitive Maps: Basic Theories and their Application to Complex Systems. In: Fuzzy Cognitive Maps, Glykas, M. (Ed.). Springer, New York, USA., ISBN: 9783642032202, pp: 1-22.
Kosko, B., 1986. Fuzzy cognitive maps. Int. J. Man Mach. Stud., 24: 65-75.
CrossRef | Direct Link | Lucchiari, C., R. Folgieri and G. Pravettoni, 2014. Fuzzy cognitive maps: A tool to improve diagnostic decisions. Diagnosis, 1: 289-293.
CrossRef | Papageorgiou, E.I. and A. Kannappan, 2012. Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification. Applied Soft Comput., 12: 3798-3809.
CrossRef | Papageorgiou, E.I., N.I. Papandrianos, G. Karagianni, G.C. Kyriazopoulos and D. Sfyras, 2009. A fuzzy cognitive map based tool for prediction of infectious diseases. Proceedings of the IEEE International Conference on Fuzzy Systems, August 20-24, 2009, Jeju Island, pp: 2094-2099.
Papageorgiou, E.I., P.P. Spyridonos, C.D. Stylios, P. Ravazoula, P.P. Groumpos and G.N. Nikiforidis, 2006. Advanced soft computing diagnosis method for tumour grading. Artif. Intell. Med., 36: 59-70.
CrossRef | Direct Link | Pratiwi, M., Alexander, J. Harefa and S. Nanda, 2015. Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput. Sci., 59: 83-91.
CrossRef | Direct Link | Salmeron, J.L., 2012. Fuzzy cognitive maps for artificial emotions forecasting. Applied Soft Comput., 12: 3704-3710.
CrossRef | Setiawan, A.S., J. Wesley and Y. Purnama, 2015. Mammogram classification using law's texture energy measure and neural networks. Procedia Comput. Sci., 59: 92-97.
CrossRef | Direct Link | Stach, W., L. Kurgan and W. Pedrycz, 2008. Data-driven nonlinear hebbian learning method for fuzzy cognitive maps. Proceedings of the IEEE International Conference on Fuzzy Systems, 2008, IEEE World Congress on Computational Intelligence, June 1-6, 2008, Hong Kong, pp: 1975-1981.
Stylios, C.S. and V.C. Georgopoulos, 2010. Fuzzy cognitive maps for medical decision support: A paradigm from obstetrics. Proceedings of IEEE 2010 Annual International Conference on Engineering in Medicine and Biology, August 31-September 4, 2010, IEEE, Buenos Aires, Argentina, ISBN: 978-1-4244-4123-5, pp: 1174-1177.
Suckling, J., J. Parker, D. Dance, S. Astley and I. Hutt
et al., 1994. The mammographic image analysis society digital mammogram database. Exerpta Med., 1069: 375-378.