FT-IR combined with PSO-CNN algorithm for rapid screening of cervical tumors

Abstract

Cervical cancer is the most common gynecological malignancy.. Early and accurate identification of the stage of cervical cancer patients can greatly improve the cure rate. In this study, serum sample data were collected from patients with cervical cancer, CIN (cervical intraepithelial neoplasia) I, CIN II, CIN III and hysteromyoma using FT-IR (Fourier-transform infrared spectroscopy) technology. PSO-CNN model for early screening of cervical cancer was designed using a particle swarm algorithm to automatically build a CNN structure with variable number of layers and variable layer class parameters. The experimental results showed that PSO-CNN was the best compared with the classical Lenet, AlexNet, VGG16 and GoogLeNet deep learning models, and the accuracy of PSO-CNN in discriminating five types of samples can reach 87.2%. This study showed that FT-IR technology combined with PSO-CNN model had great potential for non-invasive, rapid and accurate identification of patients with cervical cancer, and can provide a reference for intelligent diagnosis of other diseases.