Classification of liver tumors with CEUS based on 3D-CNN
Fengxin Pan, Qinghua Huang, Xuelong Li · 2019
Liver cancer has the third highest mortality rate in the world. Effective treatment depends on the accurate identification of benign and malignant tumors. This paper proposed a computer-aided system for distinguishing liver lesions based on CEUS, a widely accepted inspection technique. Video sequences are handled by the 3D convolutional neural network (3D-CNN) to extract spatial and temporal features. Meanwhile, the framework is trained by a specific dataset to yield a classification network. According to the results, our system obtained higher performance than the previous system. The average accuracy rate reached 93.1% with ten-fold cross-validation. It is noteworthy that the system is potential and easy to expand to other applications.