Research on classification and recognition of the skin tumors by laser ultrasound using support vector machine based on particle swarm optimization

As a common disease endangering human health, skin tumor causes millions of deaths every year. In order to effectively detect skin tumors, a laser ultrasonic detection method based on the PSO-SVM algorithm is proposed to identify and detect human skin tumors. The physical model of laser ultrasound in human skin tumors is established by FEM (finite element method), and the ultrasonic wave generated by laser in different tumor lo -cations (the skin epidemis, the dermis and between the two layers) are analyzed in detail. The minimum value, the sample entropy, the permutation entropy and the shannon entropy of laser ultrasound are extracted as the sensitive features, and the three types of tumors are classified and identified by the SVM (support vector ma-chine). The penalty factor C and kernel function parameters gamma in SVM are optimized by the PSO(particle swarm optimization) algorithm. The simulation results show that the proposed PSO-SVM algorithm has a good classi-fication effect on skin tumor prediction, which provides a new detection method for laser ultrasound to detect skin tumors.

Automated summary

In this study, a laser ultrasonic detection method based on the PSO-SVM algorithm is proposed to identify and detect human skin tumors. The physical model of laser ultrasound in human skin tumors is established, and sensitive features are extracted for classification and identification using the SVM. The simulation results demonstrate that the proposed method has a good classification effect and provides a new approach for laser ultrasound detection of skin tumors.