DOI QR코드

DOI QR Code

An Intelligent Agent System using Multi-View Information Fusion

다각도 정보융합 방법을 이용한 지능형 에이전트 시스템

  • Rhee, Hyun-Sook (Dept. of Software Engineering, Dongyang Mirae University)
  • 이현숙 (동양미래대학교 소프트웨어정보과)
  • Received : 2014.11.05
  • Accepted : 2014.11.18
  • Published : 2014.12.31

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.

본 논문에서는 데이터마이닝모듈과 정보융합모듈을 핵심구성요소로 가지는 지능형에이전트 시스템을 설계하고 다각도 정보를 융합하여 진단전문가시스템으로 활용할 수 있는 가능성을 제시한다. 데이터마이닝모듈에서는 퍼지신경망 OFUN-NET에 의하여 다각도의 데이터를 분석하고 퍼지 클러스터 정보를 지식베이스로 구축한다. 정보융합모듈과 응용모듈에서는 가능성정도로 제공되는 진단결과와 불확실 결정상태나 비대칭의 발견과 같은 전문가의 진단에 유용한 정보를 제공해 주고 있다. 또한 DDSM 벤치마크 데이터베이스로부터 획득한 디지털 유방 x선 영상의 BI-RADS 기반 특징데이터를 가지고 실험한 결과는 기존의 방법보다 높은 분류 정확도를 보여주면서 컴퓨터보조진단시스템으로서의 가능성을 보여주고 있다.

Keywords

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. https://doi.org/10.3745/KIPSTB.2007.14-B.2.135
  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.