Applied Intelligence

, Volume 30, Issue 1, pp 24–36 | Cite as

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

  • Ilias Maglogiannis
  • Elias Zafiropoulos
  • Ioannis Anagnostopoulos
Article

Abstract

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.

Keywords

Breast cancer Decision support Diagnosis Prognosis Support vector machines Bayesian classifiers Artificial neural networks 

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References

  1. 1.
    Estimated new cancer cases and deaths for 2005. http://seer.cancer.gov/cgi-bin/csr
  2. 2.
    Mangasarian et al. (1995) Breast cancer diagnosis and prognosis via linear programming. Oper Res 43(4):570–577 MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Burke HB, Goodman PH et al. (1997) Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79:857–862 CrossRefGoogle Scholar
  4. 4.
    Choong PL, de Silva CJS et al. (1996) Entropy maximization networks, an application to breast cancer prognosis. IEEE Trans Neural Netw 7(3):568–577 CrossRefGoogle Scholar
  5. 5.
    Street WN (1998) A neural network model for prognostic prediction. In: Proceedings of the fifteenth international conference on machine learning, Madison, WI. Kaufmann, Los Altos Google Scholar
  6. 6.
    Perner P, Holt A, Richter M (2005) Image processing in case-based reasoning. Knowl Eng Rev 20(3):311–314 CrossRefGoogle Scholar
  7. 7.
    Maintz J, Viergever M (1998) A survey of medical image registration. Med Image Anal 2(1):1–16 CrossRefGoogle Scholar
  8. 8.
    Tsotsos J (1985) Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system. Comput Intell 1(1):16–32 CrossRefGoogle Scholar
  9. 9.
    Martelli A (1976) An application of heuristic search methods to edge and contour detection. Commun ACM 19:73–83 MATHCrossRefGoogle Scholar
  10. 10.
    McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108 CrossRefGoogle Scholar
  11. 11.
    Metaxas D, Terzopoulos D (1991) In: Medioni G, Horn B (eds) Constrained deformable superquadrics and nonrigid motion tracking, computer vision and pattern Recognition. IEEE Computer Society, Los Alamitos, pp 337–343 Google Scholar
  12. 12.
    Staib L, Duncan J (1996) Model-based deformable surface finding for medical images. IEEE Trans Med Imaging 78(5):720–731 CrossRefGoogle Scholar
  13. 13.
    Bocchi L, Nori J (2007) Shape analysis of microcalcifications using Radon transform. Med Eng Phys 29(6):691–698 CrossRefGoogle Scholar
  14. 14.
    Amores J, Radeva P (2005) Registration and retrieval of highly elastic bodies using contextual information. Pattern Recognit Lett 26(11):1720–1731 CrossRefGoogle Scholar
  15. 15.
    Antani Sameer, Lee DJ, Long LR, Thoma GR, (2004) Evaluation of shape similarity measurement methods for spine X-ray images. J Vis Commun Image Represent 15(3):285–302 CrossRefGoogle Scholar
  16. 16.
    Zapater V, Martinez-Costa L, Ayala G (2002) Classifying human endothelial cells based on individual granulometric size distributions. Image Vis Comput 20(11):783–791 CrossRefGoogle Scholar
  17. 17.
    Martens H, Thybo AK, Andersen HJ, Karlsson AH, Donstrup S, Stodkilde-Jorgensen H, Martens M (2002) Sensory analysis for magnetic resonance-image analysis: using human perception and cognition to segment and assess the interior of potatoes. Lebensm Wiss Technol 35(1):70–79 CrossRefGoogle Scholar
  18. 18.
    Maglogiannis I, Pavlopoulos S, Koutsouris D (2005) An integrated computer supported acquisition, handling and characterization system for pigmented skin lesions in dermatological images. IEEE Trans Inf Technol Biomed 9(1):86–98 CrossRefGoogle Scholar
  19. 19.
    Morris D (1988) An evaluation of the use of texture measures for tissue characterization of ultrasound images of in vivo human placenta. Ultrasound Med Biol 14(1):387–395 CrossRefGoogle Scholar
  20. 20.
    Doukas C, Maglogiannis I, Chatzioannou A, Papapetropoulos C (2006) Automated angiogenesis quantification through advanced image processing techniques. In: Proceedings of the 28th IEEE engineering in medicine and biology conference, EMBC 2006, pp 2345–2348 Google Scholar
  21. 21.
    Anagnostopoulos, Anagnostopoulos C, Vergados D, Rouskas A, Kormentzas G (2006) The Wisconsin breast cancer problem: diagnosis and TTR/DFS time prognosis using probabilistic and generalised regression information classifiers. Oncol Rep, special issue Computational analysis and decision support systems in oncology 15:975–982 Google Scholar
  22. 22.
    Wolberg WH, Street WN, Heisey DM, Mangasarian OL (1995) Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum Pathol 26:792–796 CrossRefGoogle Scholar
  23. 23.
    Tourassi GD, Markey MK, Lo JY, Floyd CE Jr (2001) A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys 28:804–811 CrossRefGoogle Scholar
  24. 24.
    Wolberg WH, Street WN, Heisey DM, Mangasarian OL (1995) Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum Pathol 26:792–796 CrossRefGoogle Scholar
  25. 25.
    Wolberg WH, Street WN, Mangasarian OL (1994) Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Lett 77:163–171 CrossRefGoogle Scholar
  26. 26.
    Wolberg WH, Street WN, Mangasarian OL (1995) Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Anal Quant Cytol Histol 17(2):77–87 Google Scholar
  27. 27.
    Hoya T, Chambers JA (2001) Heuristic pattern correction scheme using adaptively trained generalized regression neural networks. IEEE Trans Neural Netw 12(1):91–100 CrossRefGoogle Scholar
  28. 28.
    Kaban A, Girolami M (2000) Initialized and guided EM-clustering of sparse binary data with application to text based documents. In: 15th international conference on pattern recognition, vol 2, September 2000, pp 744–747 Google Scholar
  29. 29.
    Anagnostopoulos I, Anagnostopoulos C, Loumos V, Kayafas E (2004) Classifying web pages employing a probabilistic neural network classifier. IEE Proc Softw 151(3):139–150 CrossRefGoogle Scholar
  30. 30.
    Burges C. A tutorial on support vector machines for pattern recognition. http://www.kernel-machines.org/
  31. 31.
    Schölkopf B. Statistical learning and kernel methods. http://research.Microsoft.com/~bsc
  32. 32.
    Campbell C. Kernel methods: a survey of current techniques. http://www.kernel-machines.org/
  33. 33.
    Maglogiannis IG, Zafiropoulos EP (2004) Characterization of digital medical images utilizing support vector machines. BMC Med Inform Decis Mak 4:4 CrossRefGoogle Scholar
  34. 34.
    Duan KB, Keerthi SS (2005) Which is the best multiclass SVM method? In: Oza NC et al (eds) An empirical study, MCS 2005. Lecture notes in computer science, vol 3541, pp 278–285 Google Scholar
  35. 35.
    Lee Y, Lee C-K (2003) Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19(9):1132–1139 CrossRefGoogle Scholar
  36. 36.
    Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd ed. Kaufmann, Los Altos MATHGoogle Scholar
  37. 37.
    Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1), 109–118 CrossRefGoogle Scholar
  38. 38.
    Specht DF (1996) Probabilistic neural networks and general regression neural networks. In: Chen CH (ed) Fuzzy logic and neural network handbook. McGraw-Hill, New York Google Scholar
  39. 39.
    Masters T (1995) Advanced algorithms for neural networks. Wiley, New York Google Scholar
  40. 40.
    University of Waikato. Weka software. Available at http://www.cs.waikato.ac.nz/

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ilias Maglogiannis
    • 1
  • Elias Zafiropoulos
    • 1
  • Ioannis Anagnostopoulos
    • 1
  1. 1.Department of Information and Communication Systems EngineeringUniversity of the AegeanKarlovasiGreece

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