Recognition of Thyroid Gland Cancer Cells using Fuzzy Logic and Genetic Algorithms

퍼지 논리와 유전 알고리듬을 이용한 갑상선 암세포의 인식

  • Published : 2001.06.01

Abstract

This paper proposes the new method based on fuzzy logic which recognizes between normal, and abnormal(two types of abnormal : follicular neoplastic, and papillary neoplastic) of thyroid gland cells from pre-obtained 16 feature parameters of image data. This paper applies the genetic algorithms to obtain the dominant feature parameters which have a great influence on discrimination between normal and abnormal cells. This paper shows the effectiveness of proposed method to 240 thyroid gland cells(60 normal cells, 120 follicular neoplastic cells and 60 papillary neoplastic cells) and new dominant feature parameters obtained by genetic algorithms. As a consequence of using the proposed method, average recognition rate of 88.75 % was obtained.

Keywords

References

  1. Fuzzy Logic Technology and Applications R.J.Marks II
  2. Fuzzy Models for Pattern Recognition J.C.Bezdek;S.K.Pal
  3. Fuzzy Sets, Uncertainty, And Information G.J.Klir;T.A.Folger
  4. ICNN'94 Breast Cancer Prognosis using the EMN Architecture P.L.Choong;C.J.S deSilva
  5. ICNN'94 A Comparison of Neural Networks and Fuzzy c-Means Methods in Bladder Cancer Cell Classfication Y.Hu;K.Ashenayi
  6. ICNN'94 A Hierarchical Artificial Neural Network System for the Classfication of Cervical Cells M.Bazoon;D.A.Stacey;C.Cui
  7. Histochemistry v.78 no.2 Computer analysis of Arrangement and nuclear texture in follicular thyroid tumours Kriete A;Roman W;Schaffer R(et al)
  8. Proc. of IEEE 1993 NSS and MIC Effective Discrimination of Cancer Cells in Medical Images C.H.Na;H.J.Kim
  9. Fuzzy Sets Syst. v.11 On the Design of a Classifier with Linguistic Variables as Inputs A.K.Nath;T.T.Lee
  10. IEEE Trans. Syst., Man, Cybern v.SMC-16 no.5 Fuzzy Sets Theoretic Measure for Automatic Feature Evaluation S.K.Pal;B.C.Chakraborty
  11. FUZZ-IEEE'94 v.4 Fuzzy Reasoning with Feature Cumulation as Cluster Analysis W.Homenda
  12. Fuzzy Sets Syst. v.44 Processing Uncertain Information in the Linear Space of Fuzzy Sets W.Homenda;W.Pedrycz
  13. Genetic Algorithms in Search Optimization and Machine Learning D.E.Goldberg
  14. Proc. of the Third Int. Conf. on Genetic Algorithms How Genetic Algorithms Work: A Critical Look at Implicit Parallelism J.J.Grefenstette;J.E.Baker
  15. Machine Learning 3 Learning with Genetic Algorithms: An Overview K.D.Jong
  16. FUZZ-IEEE'94 Acquisition of Fuzzy Classification Knowledge Using Genetic Algorithms H.Ishibuchi;K.Nozaki;N.Yamamoto;H.Tanaka