A CAD System for Liver Cancer Diagnosis Based on Multi-Phase CT Images Features with BP Network

Because of the difficulty of CT imaging diagnosis of hepatocellular carcinoma, it is necessary to research the system of Computer-aided Diagnosis (CAD). Most practical diagnostic schemes use single image features to input single-phase images for classification. In this paper, a diagnosis system is designed based on BP neural network after dimensionality reduction by PCA, which combines multiple features and multi-phase information. The experimental results show that the comprehensive accuracy of this system reaches 96.98%, which is superior to the single-phase CAD system and comparable with conventional CNN.