JOURNAL BROWSE
Search
Advanced SearchSearch Tips
An Intelligent Agent System using Multi-View Information Fusion
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An Intelligent Agent System using Multi-View Information Fusion
Rhee, Hyun-Sook;
  PDF(new window)
 Abstract
In this paper, we design an intelligent agent system with the data mining module and information fusion module as the core components of the system and investigate the possibility for the medical expert system. In the data mining module, fuzzy neural network, OFUN-NET analyzes multi-view data and produces fuzzy cluster knowledge base. In the information fusion module and application module, they serve the diagnosis result with possibility degree and useful information for diagnosis, such as uncertainty decision status or detection of asymmetry. We also present the experiment results on the BI-RADS-based feature data set selected form DDSM benchmark database. They show higher classification accuracy than conventional methods and the feasibility of the system as a computer aided diagnosis system.
 Keywords
intelligent agent system;computer-aided diagnosis system;data mining;information fusion;feature data;
 Language
Korean
 Cited by
 References
1.
X. Wu, X. Zhu, G-Q. Wu, and W. Ding, "Data Mining with Big Data", IEEE Trans on Knowledge and Data Engineering, Vol.26, No.1, Jan. 2014

2.
D.S. Kim, C.S. Kim, and K.W. Rim, "Modeling and Design of Intelligent Agent System", International Journal of control, Automation, and Systems, Vol. 1, No. 2, pp. 257-260, June 2003.

3.
D. Vidhate, Dr. P. Kulkarni, "Cooperative Machine Learning with Information Fusion for Dynamic Decision Making in Diagnostic Applications", Int. Conf. on Advances in Mobile Network, Communication and its Applications, 2012.

4.
J. Tang, R. M. Rangayyan, J. Xu, I. E. Naqa and Y. Yang, "Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography : Recent Advances", IEEE Trans on Information Technology in Biomedicine, Vol.13, No.2, March, 2009.

5.
M. Heath, K. Bowyer, D. Kopans, R. Moore and P. Kegelmeyer Jr., "The Digital Database for Screening Mammography", 5th IWDM, Medical Physics Publishers, 2001.

6.
Mehmed Kantardzic, "Data Mining : Concepts, Models, and Algorithms", John Wiley & Sons, 2011.

7.
Z. Vlad, M. D. Ofelia, and T-A. Maria, "Fuzzy Clustering in an Intelligent Agent for Diagnosis Establishment", Scientific Bulletin of the Petru Maior University of Tirgu Mures Vol. 6, 2009.

8.
P. Vats, "A Noval Study of Fuzzy Clustering Algorithms for their Applications in Various Domains", JICTEE, 2014.

9.
H. S. Rhee, "A Feature Selection Method Based on Fuzzy Cluster Analysis", Journal of Korea Information Processing Society, Vol.14-B, No.2, pp.135-140, 2007. crossref(new window)

10.
Wu, Y., He, J., Man, Y., & Arribas, J.I., "Neural Network Fusion Strategies for Identifying Breast Masses", proc. of the IEEE International Joint Conference on Neural Networks(IEEE-IJCNN'2004), 2004.

11.
R. Panchal and B. Verma, "Characterization of breast abnormality patterns in digital mammograms using autoassociator neural network," in International Conference on Image Processing 2006, Part III, LNCS, vol. 4234, pp. 127-136, Springer-Verlag, 2006.

12.
Brijesh Verma and John Zakos, A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques, IEEE Trans. on Information Technology in Biomedicine, vol. 5, no. 1, march 2001.

13.
M. Radovic, M. Djokovic, A. Peulic, and N. Filipovic, "Application of Data Mining Algorithms for Mammogram Classification", 13th conf. on Bioinformatics and Bioengineering(BIBE), 2013.

14.
L.Sun, L. Li, W. Xu, W. Liu, J. Zhang, and G. Shao, "A Novel Classification Scheme for Breast Masses Based on Multi-view Information Fusion", 4th Int. Conf. on Bioinformatics & Biomedical Engineering (iCBBE), 2010.

15.
H. Zhao, W. Xu, L. Li, and J. Zhang, "Classification of Breast Masses Based on Multi-view Information Fusion Using Multi-Agent Method", 5th Int. Conf. on Bioinformatics & Biomedical Engineering (iCBBE), 2011.

16.
Reference URL : http://marathon.csee.usf.edu/Mammography/Database.html

17.
J.Y. Lo, et al., "Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists", Proc. SPIE 5032, Medical Imaging, 2003.

18.
Dheeda J. and T. Selvi.S, "Classification of Malignant and Benign Microcalcification Using SVM Classifier", Proc. of ICETECT, 2011.