Mammographic mass classification using wavelet based support vector machine

GÖRGEL P., SERTBAŞ A., Kilic N., Ucan O. N., Osman O.

Istanbul University - Journal of Electrical and Electronics Engineering, cilt.9, sa.1, ss.867-875, 2009 (Scopus, TRDizin) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 1
  • Basım Tarihi: 2009
  • Dergi Adı: Istanbul University - Journal of Electrical and Electronics Engineering
  • Derginin Tarandığı İndeksler: Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.867-875
  • Anahtar Kelimeler: Discrete Wavelet Transform, Mammographic Mass Classification, Support Vector Machine, k-fold Cross Validation
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

In this paper, we investigate an approach for classification of mammographic masses as benign or malign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-based subband image decomposition. Decision making was performed in two stages as feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. SVM, a learning machine based on statistical learning theory, was trained through supervised learning to classify masses. The research involved 66 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. Preliminary test on mammogram showed over 84.8% classification accuracy by using the SVM with Radial Basis Function (RBF) kernel. Also confusion matrix, accuracy, sensitivity and specificity analysis with different kernel types were used to show the classification performance of SVM.