Low-Rank Matrix Denoising Algorithm-Based MRI Image Diagnosis of Uterine Malignant Tumor and Postoperative Care

Liqiong Cao,Yanling Liu,Huiting Zhang,M Pallikonda Rajasekaran

doi:10.1155/2022/2863893

Abstract

This paper aimed to discuss regarding diagnosis and postoperative care of uterine malignant tumor, the effect of MRI image based on low-rank matrix denoising algorithm in diagnosis, and postoperative care of uterine malignant tumor. 100 patients with uterine malignant tumor are selected for MRI examination and the MRI examination based on low-rank matrix denoising algorithm. The accuracy, sensitivity, and specificity of the two kinds of MRI are evaluated and compared by three or more experienced doctors through a double-blind method. At the same time, under the guidance of MRI image after denoising, relevant postoperative care is carried out. The results are compared with the previous results in our hospital. The results showed that the sensitivity, specificity, and accuracy of denoised MRI images in the diagnosis of uterine malignant tumors are higher than those of ordinary MRI. After denoising, the postoperative nursing guided by the MRI image effectively reduces the occurrence of postoperative complications. In postoperative nursing, the overall satisfaction of patients with nursing increases by 10.9%. Conclusion. The MRI image based on the low-rank matrix denoising algorithm has an obvious effect on diagnosis and postoperative care of uterine malignant tumor.

Highlights

Ung et al (2018) found that the denoising algorithm based on low rank matrix has better denoising effect than other latest denoising algorithms [7]
The patients were given the postoperative care under the guidance of Magnetic resonance imaging (MRI) based on the low-rank matrix denoising algorithm, and the difference between the care data of uterine malignant tumor and that of the hospital before was observed. e two methods were compared to show the role of the MRI image based on low rank matrix denoising algorithm in the diagnosis of uterine malignant tumor and postoperative care
In the research of MRI image denoising, some studies suggest that the denoising method based on low-rank matrix has a better denoising effect on MRI image [15]. erefore, the role of MRI based on the low-rank matrix denoising algorithm in the diagnosis and postoperative care of uterine malignant tumor has been studied

Summary

Introduction

Ung et al (2018) found that the denoising algorithm based on low rank matrix has better denoising effect than other latest denoising algorithms [7]. E research of the above scholars shows that the denoising algorithm based on low-rank matrix has a good effect in image denoising. It is not commonly used in MRI image denoising. Erefore, the research innovation is that MRI images play a certain role in the diagnosis of uterine tumors. Erefore, the algorithm based on low-rank matrix is used to effectively remove the noise in MRI images to significantly improve the quality of MRI images and help doctors diagnose diseases more accurately. Erefore, the role of MRI image based on the low-rank matrix denoising algorithm in the diagnosis of uterine malignant tumor and postoperative care is studied Scientific Programming clinicians’ judgment of uterine tumors. erefore, the algorithm based on low-rank matrix is used to effectively remove the noise in MRI images to significantly improve the quality of MRI images and help doctors diagnose diseases more accurately. erefore, the role of MRI image based on the low-rank matrix denoising algorithm in the diagnosis of uterine malignant tumor and postoperative care is studied

Methods

Results

Discussion

Conclusion