The Contrast-Enhanced Ultrasound Information Technology in the Treatment of Liver Tumors

Objective: To study the automatic diagnosis method of liver tumors in the contrast-enhanced ultrasound environment, assist doctors in the clinical diagnosis of liver tumors intuitively, conveniently, and accurately, thereby improve the cure rate of liver tumors. Methods: First, six sets of experimental data were constructed. The automatic diagnosis experiment of liver tumors through contrast-enhanced ultrasound was performed by the combination of sparse representation-based support vector machine (SVM) and principal component analysis (PCA)-based SVM, as well as the sparse representation classification method. The effect of classification decision principles on experiments was further studied. Results: The SVM method had an average effect on diagnostic accuracy. The average diagnostic accuracy of the six different experimental data sets was 76%, and the average diagnosis time was 300 s. The feature extraction method based on the combination of sparse representation and PCA was applied to the SVM method to achieve an optimal diagnosis. The average diagnosis accuracy rate could reach 87%, and the average time was more than 1,000 s. Using the sparse classification representation method, the diagnostic accuracy rate for the six experimental data sets constructed was above 93%, with a maximum of 99%, and the average time was 210 s. The sparse classification representation using the principle of minimum reconstruction error classification decision had an average diagnostic accuracy rate of 99% and an average time of 128 s. Conclusion: The sparse classification representation for the clinical diagnosis of liver tumors by contrast-enhanced ultrasound had high accuracy and consumed less time. Therefore, the constructed method was valuable.