Tehran University of Medical Sciences Basic & Clinical Cancer Research 2228-6527 10 1 2018 04 18 34 41 EN Bahman Cheraghi Gharakhanloo Department of computer enginering , Karaj Branch, Islamic Azad University, Karaj, Iran Bashir Bagheri Nakhjavanlo Department of Mathematics and Computer , Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran Ali Mohammad Mohammadi Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran. 2017 10 04 2018 02 28 Diagnosis of the tumors' tissues in the liver and distinguishing the malignant tumors from benign is a critical issue in medicine. In this regard, so many methods have been proposed to make the accurate tumor detection and classification algorithms using Machine Learning and Computer Vision techniques. In this study, first we analyzed the liver’s MR images using Discrete Wavelet Transform techniques for dimensionality reduction and feature extraction, and then Principal Component Analysis technique has been employed to select the essential features for classification, and finally the selected features were used to train Support Vector Machine algorithm. In classification, we used the different kernels for SVM and the result of each classifier was compared. The outcome of the algorithm indicates the high performance of our method when there are few training data available https://bccr.tums.ac.ir/index.php/bccrj/article/view/259 https://bccr.tums.ac.ir/index.php/bccrj/article/download/259/479