Automatic classification method of liver ultrasound standard plane images using pre-trained convolutional neural network - Jiaxiang Wu; Pan Zeng; Peizhong Liu; Guorong Lv (Connection Science)
The liver ultrasound standard planes (LUSP) have significant diagnostic significance during ultrasonic liver diagnosis. However, the location and acquisition of LUSP could be a time-consuming and complicated mission and requires the relevant operator to have comprehensive knowledge of ultrasound diagnosis. Therefore, this study puts forward an automatic classification approach for eight types of LUSP based on a pre-trained CNN(Convolutional Neural Network). With the comparison to classification methods on the basis of conventional hand-craft characteristics, the method proposed by us can automatically catch the appearance in LUSP and classify the LUSP. The proposed model is consisted of 13 convolutional layers with little 3×3 size kernels and three completely connected layers. To address the limitation of data, we adopt the transfer learning strategy, which pre-trains the weight of convolutional layers and fine-tune the weight of fully connected layers. These extensive experiments show that the accuracy of the suggested method reaches 92.31%, as well as the performance of the suggested means outperforms previous ways, which demonstrates the suitability and effectiveness of CNN to classify LUSP for clinical diagnosis.