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Journals / Electrica / 2009 / Cilt: 9 Sayı: 1

MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

Pelin GORGEL
Ahmet SERTBAŞ
Niyazi KILIC
Osman N. UCAN
Onur OSMAN
Journal
Electrica
Pages
867–875
Publication Date
2012-02-14
DOI
—
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MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

📌 3 Key Takeaways

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Objective & Hypothesis:MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE
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Key Findings & Data:MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE
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Conclusion & Impact:MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Discrete Wavelet Transform · Mammographic Mass Classification · Support Vector

Abstract

MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

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Cite This Article

GORGEL, P., SERTBAŞ, A., KILIC, N., UCAN, O. N., OSMAN, O. (2012). MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. Electrica, Cilt: 9 Sayı: 1, 867–875. https://ajindex.com/articles/1017552

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References (19)

  1. B. Sahiner, H.P. Chan, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images”, IEEE T. Med Imaging, 15:598- 610, 1996
  2. C.C. Boring, T.S. Squires, “Cancer statistics”, CA-A Cancer J Clin, 44:7-26, 1994
  3. H.C. Zuckerman, “The role of mammograph in the diagnosis of breast cancer, in Breast Cancer, Diagnosis and Treatment”, I. M. Ariel and J. B. Cleary. Eds. McGraw-Hill, New York, 1987
  4. H.P. Chan, K. Doi, “Improvement in radiologists’ detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis”, Invest Radiol, 25:1102-1110,1990
  5. Wang T C, Karayiannis N B, “Detection of microcalcifications in digital mammograms using wavelets” IEEE T Med Imaging, 17:498- 509, 1989
  6. Bruce L M, Adhami R R, “Classifying mammographic mass shapes using the wavelet transform modulus-maxima method”, IEEE T Med Imaging 18:1170-1177, 1999
  7. S.G. Mallat, “A theory of multiresolution signal decomposition: the wavelet representation”, IEEE T. Pattern Anal, 1980, 11:674– 693
  8. R.N. Bracewell, “The Fourier Transform and its Applications”, McGraw-Hill, New York, 1999
  9. V. Vapnik, “Statistical learning theory”, Wiley, New York, 1998
  10. K.R. Muller, S. Mika, “An introduction to kernel-based learning algorithms”, IEEE T., Neural Network, 2001, 12:181-201
  11. I. El-Naqa, Y. Yang, “A support vector machine approach for detection of microcalcifications in mammogram”, IEEE T. Med Imaging, 2002, 21:1552-1563
  12. C.M. Brislawn, “Fingerprints go digital”, Notices Amer Math Soc., 1995, 42:1278-1283
  13. M. Al-qdah, A.R. Ramli, “Detection of calcifications in mammography using wavelets”, Student Conference on Research and Development (SCORcD) Proceedings, 2003, Putrajaya, Malaysia
  14. Mathsoft Wavefet resources, A great collection of theory and application oriented articles on the web at http://mw.mthsof.cod wavelets.html, 1997
  15. S. Chaplot, L.M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Biomedical Signal Processing and Control, 2006, 1:86-92
  16. R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, New Jersey, 2002
  17. Koenderink J (1984) The structure of images. Biol Cybern 50:363-370
  18. V.N. Vapnik, “An overview of statistical learning theory”, IEEE T. Neural Network, 1999 10:988-999
  19. K. Polat, S. Guneş, “Breast cancer diagnosis