Computer-aided diagnosis for feature selection and classification of liver tumors in computed tomography images
Wen-Jia Kuo · 2018 IEEE International Conference on Applied System Invention (ICASI) · 2018
We propose a computer-aided diagnosis (CAD) system to classify liver tumors in non-enhanced computed tomography (CT) images. There are three parts in our proposed system. First, the feature extraction module extracts 102 statistical texture features. Second, the feature selection module acquired the combination of the best features by integrating the particle swarm optimization (PSO) algorithm with support vector machine (SVM) to reduce the complexity of computation. Finally, a SVM based classification model was constructed to identify benign and malignant liver tumors. Experiments show the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the proposed CAD system for classifying liver tumors was 84.53%, 80%, 88.09%, 88.77%, and 84.01%, respectively. The accurate rate is up to 80 % both on benign and malignant tumors of CT images. We can find that the proposed method can achieve the purpose of enhancing the accuracy of automatic identify effectively to assist further diagnosis.