A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks

Publication date: Available online 26 May 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Jing Li, Yurun Wu, Nanyan Shen, Jiawen Zhang, Enlong Chen, Jie Sun, Zongqian Deng, Yuchen ZhangAbstractThe cancer of liver, which is the leading cause of cancer death, is commonly diagnosed by comparing the changes of gray level of liver tissue in the different phases of the patient's CT images. To aid the doctor in reducing misdiagnosis or missed diagnosis, a fully automatic computer-aided diagnosis (CAD) system is proposed to diagnose hepatocellular carcinoma (HCC) using convolutional neural network (CNN) classifier. The automatic segmentation and classification are two core technologies of the proposed CAD system, which are both realized based on CNN. The segmentation of liver and tumor is implemented by a fully convolutional networks (FCN) based on a fine tuning VGG-16 model with two additional ‘skip structures’ using a weighted loss function which helps to solve the problem of inaccurate tumor segmentation caused by the inevitably unbalanced training data. HCC classification is implemented by a 9-layer CNN classifier, whose input is a 4-channel image data constructed by combining the segmentation result of FCN with the original CT image. A total of 165 venous phase CT images including 46 diffuse tumors, 43 nodular tumors, and 76 massive tumors are used to evaluate the performance of the proposed CAD system. The classification accuracy of CNN classifier for diffus...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research