TY - JFULL AU - Metehan Makinacı PY - 2007/8/ TI - Support Vector Machine Approach for Classification of Cancerous Prostate Regions T2 - International Journal of Medical and Health Sciences SP - 469 EP - 473 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/11474 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 7, 2007 N2 - The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance. ER -