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CLASSIFICATION OF BREAST MASSES USING ANFIS-BASED FUZZY ALGORITHMS: A COMPARATIVE STUDY

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2013

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
This study aims to produce a diagnosis system for breast masses related to breast cancer. The dataset consisting of 60 digital mammograms is acquired from Istanbul University Faculty of Medicine Hospital. 78 masses in the mammograms are extracted manually for this study by the experts. It is a fuzzy based comperative study of malignant-benign classification for breast masses which has the accuracy of 74.36% with k-means and 93.75% with ANFIS based fuzzy c-means and subtractive clustering.
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FULL TEXT (PDF): 
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  • 1
1605-1611
  • English

REFERENCES

References: 

[1] In-Sung J., Devinder T. and Wang G.N. Neural Network Based Algorithms for diagnosis and classification of breast canser tumor. Department of Industrial and Information Engineerin, Ajou University, South Korea, 2011.
[2] Gorgel P. , “Cancer Region diagnosis of 2-dimensional mammographic data using image processing techniques”, İstanbul University, The Institute of Sciences, Computer Engineering Department, PhD thesis, 2011.
[3] DUNN, J.C., 1974, A Fuzyy Relative of ISODATA Process and Its Use in Detecting Compact, Well Separated Clusters, Journ., Cybern., 3, 95-104.
[4] BEZDEK, J.C., 1981, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York.
[5] Hongxing, L. et al. 2001. Fuzzy Neural Intelligent System, Mathematical Foundation and the Application in Engineering. CRC Press LLC.
[6] SUGENO, M., 1977, "Fuzzy measures and fuzzy integrals: a survey," (M.M. GUPTA, G. N. SARIDIS, and B.R. GAINES, editors) Fuzzy Automata and Decision Processes, pp. 89-102, North-Holland, NY.
[7] JANG, J.-S. R. and C.-T. SUN, 1997, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Prentice Hall.
[8] Geisser, Seymour (1993). Predictive Inference. New York, NY: Chapman and Hall. ISBN 0412034719.

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