Advertisement

A CAD System for Breast Cancer Diagnosis Using Modified Genetic Algorithm Optimized Artificial Neural Network

  • J. Dheeba
  • S. Tamil Selvi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

In this paper, a computerized scheme for automatic detection of cancerous tumors in mammograms has been examined. Diagnosis of breast tumors at the early stage is a very difficult task as the cancerous tumors are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm – Modified Genetic Algorithm (MGA) tuned Artificial Neural Network for detection of tumors in mammograms. Genetic Algorithm is a population based optimization algorithm based on the principle of natural evolution. By utilizing the MGA, the parameters of the Artificial Neural Network (ANN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the cancerous tissues and normal tissues prior to classification. Then Modified Genetic Algorithm (MGA) tuned Artificial Neural Network classifier is applied at the end to determine whether a given input data is suspicious for tumor or not. The performance of our computerized scheme is evaluated using a database of 322 mammograms originated from MIAS databases. The result shows that the proposed algorithm has a recognition score of 97.8%.

Keywords

Microcalcification Mammograms Computer Aided Detection Neural Network Texture Energy Measures Genetic Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yu, S., Guan, L.: A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films. IEEE Transactions on Medical Imaging 19(2), 115–126 (2000)CrossRefGoogle Scholar
  2. 2.
    Netsch, T., Peitgen, H.O.: Scale-Space Signatures for the Detection of Clustered Microcalcifications in Digital Mammograms. IEEE Transactions on Medical Imaging 18(9), 774–786 (1999)CrossRefGoogle Scholar
  3. 3.
    Sahiner, B., et al.: Classificaiton of Mass and Normal Breast Tissue: A convolution Neural Network classifier with spatial domain and Texture Images. IEEE Trans. on Medical Imaging 15(5), 598–609 (1996)CrossRefGoogle Scholar
  4. 4.
    Cascio, D., et al.: Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network. IEEE Trans. on Nuclear Science 53(5), 2827–2833 (2006)CrossRefGoogle Scholar
  5. 5.
    Kim, J.K., Park, H.W.: Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms. IEEE Trans. on Medical Imaging 18(3), 231–238 (1999)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Chang, C.: New Texture shape feature coding based computer aided diagnostic methods for classification of masses on mammograms. In: 26th IEEE Annual Int. Conference of the Engg. In Medicine and Biology Society IEMBS, vol. 1, pp. 1275–1281 (2004)Google Scholar
  7. 7.
    Gao, X., Wang, Y., Li, X., Tao, D.: On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection. IEEE Trans. on Information Technology in Biomedicine 14(2), 266–273 (2010)CrossRefGoogle Scholar
  8. 8.
    Nishikawa, R.M., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A.: Computer-Aided Detection of Clustered Microcalcifications on Digital Mammograms. Medical and Biological Engineering and Computing 33(2), 174–178 (1995)CrossRefGoogle Scholar
  9. 9.
    Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E.R.E., Zwiggelaar, R.: A Review of Automatic Mass Detection and Segmentation in Mammographic Images. Journal of Medical Image Analysis 14, 87–110 (2010)CrossRefGoogle Scholar
  10. 10.
    Wang, D., Shi, L., Heng, P.A.: Automatic Detection of Breast Cancers in Mammograms Using Structured Support Vector Machines. Journal of Neurocomputing 72, 3296–3302 (2009)CrossRefGoogle Scholar
  11. 11.
    Christodoulou, C.I., Michaelides, S.C., Pattichis, C.S.: Multifeature Texture Analysis for the Classification of Clouds in Satellite Imagery. IEEE Transactions on Geoscience And Remote Sensing 41(11), 2662–2668 (2003)CrossRefGoogle Scholar
  12. 12.
    Anna, N., Ioannis, S., Spyros, G., Filippos, N.: Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications. IEEE Trans. on Information Technology in Biomedicine 12(6), 731–738 (2008)CrossRefGoogle Scholar
  13. 13.
    Reed, R.T., du Buf, J.M.H.: A review of recent texture segmentation and feature extraction techniques. Comput. Vis. Graphics Image Processing 57(3), 359–372 (1993)CrossRefGoogle Scholar
  14. 14.
    Verma, B., Zakos, J.: A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine 5(1), 46–54 (2001)CrossRefGoogle Scholar
  15. 15.
    Chiang, C.-L.: Improved Genetic Algorithm for Power Economic Dispatch of Units with Valve-Point Effects and Multiple Fuels. IEEE Transactions on Power Systems 20(4), 1690–1699 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Suckling, J., Parker, J.: The Mammographic Images Analysis Society Digital Mammogram Database. In: Proc. of 2nd Int. Workshop Digital Mammography, U.K, pp. 375–378 (1994)Google Scholar
  17. 17.
    Rajasekaran, S., Vijayalakshmi Pai, G.A.: Neural Networks, Fuzzy Logic and Genetic Algorithms. Prentice Hall of India (2000)Google Scholar
  18. 18.
    Dheeba, J, Tamil Selvi, S.: Screening Mammogram Images for Abnormalities using Radial Basis Function Neural Network. In: IEEE International Conference- ICCCCT 2010, pp. 554–559 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • J. Dheeba
    • 1
  • S. Tamil Selvi
    • 2
  1. 1.Department of Computer Science and EngineeringNoorul Islam UniversityKumaracoilIndia
  2. 2.Department of Electronics and Communication EngineeringNational Engineering CollegeKovilpattiIndia

Personalised recommendations