Support vector machine based liver cancer early detection using magnetic resonance images

Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic resonance (MR) images. We apply the histogram-based feature extraction method to extract feature information from each raw MR image acquired. And 100 confirmed liver cancer and 100 confirmed benign type liver tumor (BLT) patients' feature information are used to form our training data set to train or SVM classification engine. The model is tested with a set of 30 confirmed early stage liver cancer and 30 BLT samples. Our trained SVM achieves an accuracy of 86.67% in classifying early stage liver cancer and 80.00% in classifying BLT.