Efficacy of liver cancer microwave ablation through ultrasonic image guidance under deep migration feature algorithm.

Changkong Ye,Wenyan Zhang,Zijuan Pang,Wei Wang

doi:10.12669/pjms.37.6-wit.4885

Abstract

Objective:To explore the therapeutic effects of ultrasound-guided microwave ablation and radio frequency ablation for liver cancer patients.Methods:Seventy-eight patients with microwave ablation were rolled into the experimental group and 56 patients with radio frequency ablation were in the control group. This study was conducted from March 1, 2019 to June 30, 2020 in our hospital. Based on Convolutional Neural Networks (CNN) and Migration feature (MF), a new ultrasound image diagnosis algorithm CNNMF was constructed, which was compared with AdaBoost and PCA-BP based on Principal component analysis (PCA) and back propagation (BP), and the accuracy (Acc), specificity (Spe), sensitivity (Sen), and F1 values of the three algorithms were calculated. Then, the CNNMF algorithm was applied to the ultrasonic image diagnosis of the two patients, and the postoperative ablation points, complications and ablation time were recorded.Results:The Acc (96.31%), Spe (89.07%), Sen (91.26%), and F1 value (0.79%) of the CNNMF algorithm were obviously larger than the AdaBoost and the PCA-BP algorithms (P< 0.05); in contrast with the control group. The number of ablation points in the experimental group was obviously larger, and the ablation time was obviously shorter (P<0.05); the experimental group had one case of liver abscess and two cases of wound pain after surgery, which were both obviously less than the control group (four cases; five cases) (P<0.05)Conclusion:In contrast with traditional algorithms, the CNNMF algorithm has better diagnostic performance for liver cancer ultrasound images. In contrast with radio frequency ablation, microwave ablation has better ablation effects for liver cancer tumors, and can reduce the incidence of postoperative complications in patients, which is safe and feasible.

Highlights

At present, deep learning has been widely used in the field of medical image processing, and the most common deep learning method is convolutional neural network.[1,2,3] it greatly reduces the learning performance of deep learning due to the limitation of the sample size of medical images.[4,5,6]A new ultrasound image diagnosis algorithm CNNMF was constructed based on Convolutional Neural Networks (CNN) and Migration feature (MF), and applied to 78 patients with microwave ablation and 56 patients with radio frequency ablation
Pak J Med Sci September 2021 (Speical Issue Online) Vol 37 No 6 www.pjms.org.pk 1693 the CNNMF, AdaBoost, and Principal component analysis (PCA)-back propagation (BP) algorithms and the ablation points and ablation time, the ultrasound-guided microwave ablation and radio frequency ablation were comprehensively evaluated for the treatment effects
Seventyeight cases of microwave ablation were rolled into the experimental group, and 56 patients with radio frequency ablation were included into the control group

Summary

Introduction

Deep learning has been widely used in the field of medical image processing, and the most common deep learning method is convolutional neural network.[1,2,3] it greatly reduces the learning performance of deep learning due to the limitation of the sample size of medical images.[4,5,6]. A new ultrasound image diagnosis algorithm CNNMF was constructed based on CNN and MF, and applied to 78 patients with microwave ablation and 56 patients with radio frequency ablation. By comparing the Acc, Spe, Sen, and F1 values of. Pak J Med Sci September 2021 (Speical Issue Online) Vol 37 No 6 www.pjms.org.pk 1693 the CNNMF, AdaBoost, and PCA-BP algorithms and the ablation points and ablation time, the ultrasound-guided microwave ablation and radio frequency ablation were comprehensively evaluated for the treatment effects

Methods

Results

Discussion

Conclusion