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Prostate Disease Diagnosis from CT Images Using Multi-Class Support Vector Machine


Wafaa A. Abbas, Salema S. Salman and Ban S.Ismael


Vol. 19  No. 1  pp. 121-127


Prostate disease is very common now men (adult and advanced in years old), all patients of prostate disease are having similar symptoms, it is difficult to diagnose malignant prostate at an early stage because of the noise corrupts medical images of CT scan. In this study bilateral filtering, Image sharpening and contrast stretching are implemented for medical image denoising,edge enhancement and evaluation of medical image quality respectively, to enhance medical images which correlated with early diagnosis of prostate cancer from CT image using multiclass support vector machine (SVM) classification method. Thirteen features extracted from 20 ?20-pixel block of each slice of CT that the prostate appears in it, which is used later for the training and test SVM approach. xperimental results demonstrate that the SVM approach gives the best performance to the classification between normal and abnormal prostate by 100%, while Multi-SVM is not quite appropriate to identify the prostate cancer where the medical diagnosis using CT scan succeeded by 65% in identifying cancer. rostate size calculated for Iraqi normal adults, by two methods, ellipsoid approach and frustum Approach where the prostate size varies with age, it becomes significantly larger in older men. The prostate gland tends to enlarge, around the age of 40. The two methods gave success in the computation of size using CT image, where they showed a significant match in the results.


Malignant Prostate, CT image, SVM, Diagnosis prostate cancer.