New Methodology of Computer Aided Diagnostic System on Breast Cancer (original) (raw)

Abstract

In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.

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Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering, Korea University, Seoul, Korea
    HeeJun Song, SeonGu Lee, Dongwon Kim & GwiTae Park

Authors

  1. HeeJun Song
  2. SeonGu Lee
  3. Dongwon Kim
  4. GwiTae Park

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
    Jun Wang
  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China
    Xiao-Feng Liao
  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China
    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Song, H., Lee, S., Kim, D., Park, G. (2005). New Methodology of Computer Aided Diagnostic System on Breast Cancer. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469\_124

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