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Discrimination of Benign from Malignant Breast Lesions Using Statistical Classifiers

  • Konstantinos Koutroumbas
  • Abraham Pouliakis
  • Tatiana Mona Megalopoulou
  • John Georgoulakis
  • Anna-Eva Giachnaki
  • Petros Karakitsos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)

Abstract

The objective of this study is to investigate the discrimination of benign from malignant breast lesions using: the linear, the feedforward neural network, the k-nearest neighbor and the boosting classifiers. Nuclear morphometric parameters from cytological smears taken by Fine Needle Aspiration (FNA) of the breast, have been measured from 193 patients. These parameters undergo an appropriate transformation and then, the classifiers are performed on the raw and on the transformed data. The results show that in terms of the raw data set all classifiers exhibit almost the same performance (overall accuracy  ≡ 87%), Thus the linear classifier suffices for the discrimination of the present problem. Also, based on the previous results, one can conjecture that the use of these classifiers combined with image morphometry and statistical techniques for feature transformation, may offer useful information towards the improvement of the diagnostic accuracy of breast FNA.

Keywords

Fine Needle Aspiration Breast Lesion Small Cell Carcinoma Giemsa Stain Malignant Breast Lesion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Konstantinos Koutroumbas
    • 1
  • Abraham Pouliakis
    • 2
  • Tatiana Mona Megalopoulou
    • 2
  • John Georgoulakis
    • 3
  • Anna-Eva Giachnaki
    • 4
  • Petros Karakitsos
    • 3
  1. 1.Institute for Space Applications and Remote SensingNational Observatory of AthensGreece
  2. 2.Department of Histology and Embryology, Medical School of AthensAthens UniversityGreece
  3. 3.Department of CytopathologyAttikon University HospitalAthensGreece
  4. 4.Hellenic Cancer SocietyGreece

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