BibTex RIS Kaynak Göster
Yıl 2012, Cilt: 2 Sayı: 4, 327 - 333, 01.12.2012

Öz

Kaynakça

  • In-Sung J., Devinder T. and Wang G.N. Neural Network Based Algorithms for diagnosis and classification of breast canser tumor. Department of Industrial and University, South Korea, 2011. Ajou
  • Gorgel P. , “Cancer Region diagnosis of 2-dimensional mammographic data using image processing techniques”, İstanbul University, The Institute of Sciences, Computer Engineering Department, PhD thesis, 2011.
  • P. Delogu, M. E. Fantacci, P. Kasae, A. mammographic masses using a gradient- based segmentation algorithm and a neural classifier”, Computers in Biology and Medicine, vol. 37, pp. 1479 – 1491, 2007. of
  • R. M. Rangayyan, N. R. Mudigonda, J. E. L. Desautels, “Boundary modelling and shape analysis methods for classification of mammographic masses”, Medical and Biological Engineering and Computing, vol. 38, no.5, pp. 487-496, 2000.
  • D. Cascio, F. Fauci, R. Magro, G. Raso, “Mammogram segmentation by contour searching and mass lesions classification with neural network ”, IEEE Transactions on Nuclear Science, vol.53, no.5, pp. 2827-2833, 2006.
  • D. Tralic, J. Bozek, S. Grgic, "Shape analysis and classification of masses in mammographic images using neural networks", 18th International Conference on Systems, Signals and Image Processing (IWSSIP), Sarajevo, 2011.
  • S. Mallat, “A compact multiresolution representation: The wavelet model”, IEEE Computer Society Workshop Washington, 1987. Vision, Processing using MATLAB,
  • M. Alata, M. Molhim, “Optimizing of fuzzy C-means clustering Algorithm using GA”, World Academy of Science, Engineering and Technology, vol. 39, 2008.
  • A. Elmzabi, M. Bellafkih, M. Ramdani, “An adaptive fuzzy clustering approach for the network management”, World Academy of Science, Engineering and Technology, vol.31, pp. 425-430, 2007.
  • P. Tahmasebi, A. Hezarkhani, “Application of Adaptive Neuro- Fuzzy Inference System for Grade Estimation; Case Study”, Australian Journal of Basic and Applied Sciences, vol.4, no.3, pp. 408- 420, 2010.
  • V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998
  • C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Kluwer Academic Publishers, Dordrecht, 1998.
  • G. B. Junior, A. C. Paiva et al., Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM, Computers in Biology and Medicine, 39 (2009), 1063 -1072.
  • J. Yan, B. Zhang, N. Liu et al., Effective and efficient dimensionality reduction for Large-Scale Preprocessing, IEEE Transactions on Knowledge and Data Engineering, 18 (2006), 320-333. Streaming Data

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

Yıl 2012, Cilt: 2 Sayı: 4, 327 - 333, 01.12.2012

Öz

This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with preprocessing by using feature extraction based Fast Wavelet Transform (FWT). Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS) based fuzzy subtractive clustering and Support Vector Machines (SVM) methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies

Kaynakça

  • In-Sung J., Devinder T. and Wang G.N. Neural Network Based Algorithms for diagnosis and classification of breast canser tumor. Department of Industrial and University, South Korea, 2011. Ajou
  • Gorgel P. , “Cancer Region diagnosis of 2-dimensional mammographic data using image processing techniques”, İstanbul University, The Institute of Sciences, Computer Engineering Department, PhD thesis, 2011.
  • P. Delogu, M. E. Fantacci, P. Kasae, A. mammographic masses using a gradient- based segmentation algorithm and a neural classifier”, Computers in Biology and Medicine, vol. 37, pp. 1479 – 1491, 2007. of
  • R. M. Rangayyan, N. R. Mudigonda, J. E. L. Desautels, “Boundary modelling and shape analysis methods for classification of mammographic masses”, Medical and Biological Engineering and Computing, vol. 38, no.5, pp. 487-496, 2000.
  • D. Cascio, F. Fauci, R. Magro, G. Raso, “Mammogram segmentation by contour searching and mass lesions classification with neural network ”, IEEE Transactions on Nuclear Science, vol.53, no.5, pp. 2827-2833, 2006.
  • D. Tralic, J. Bozek, S. Grgic, "Shape analysis and classification of masses in mammographic images using neural networks", 18th International Conference on Systems, Signals and Image Processing (IWSSIP), Sarajevo, 2011.
  • S. Mallat, “A compact multiresolution representation: The wavelet model”, IEEE Computer Society Workshop Washington, 1987. Vision, Processing using MATLAB,
  • M. Alata, M. Molhim, “Optimizing of fuzzy C-means clustering Algorithm using GA”, World Academy of Science, Engineering and Technology, vol. 39, 2008.
  • A. Elmzabi, M. Bellafkih, M. Ramdani, “An adaptive fuzzy clustering approach for the network management”, World Academy of Science, Engineering and Technology, vol.31, pp. 425-430, 2007.
  • P. Tahmasebi, A. Hezarkhani, “Application of Adaptive Neuro- Fuzzy Inference System for Grade Estimation; Case Study”, Australian Journal of Basic and Applied Sciences, vol.4, no.3, pp. 408- 420, 2010.
  • V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998
  • C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Kluwer Academic Publishers, Dordrecht, 1998.
  • G. B. Junior, A. C. Paiva et al., Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM, Computers in Biology and Medicine, 39 (2009), 1063 -1072.
  • J. Yan, B. Zhang, N. Liu et al., Effective and efficient dimensionality reduction for Large-Scale Preprocessing, IEEE Transactions on Knowledge and Data Engineering, 18 (2006), 320-333. Streaming Data
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA78UU73HY
Bölüm Makaleler
Yazarlar

Pelin Gorgel Bu kişi benim

Ahmet Sertbas Bu kişi benim

Osman N. Ucan Bu kişi benim

Yayımlanma Tarihi 1 Aralık 2012
Yayımlandığı Sayı Yıl 2012 Cilt: 2 Sayı: 4

Kaynak Göster

APA Gorgel, P., Sertbas, A., & Ucan, O. N. (2012). FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION. International Journal of Electronics Mechanical and Mechatronics Engineering, 2(4), 327-333.