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Low-Rank Matrix Denoising Algorithm-Based MRI Image Diagnosis of Uterine Malignant Tumor and Postoperative Care
Author(s) -
Liqiong Cao,
Huiting Zhang,
Yanling Liu
Publication year - 2022
Publication title -
scientific programming
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 36
eISSN - 1875-919X
pISSN - 1058-9244
DOI - 10.1155/2022/2863893
Subject(s) - medicine , radiology , noise reduction , rank (graph theory) , algorithm , matrix (chemical analysis) , artificial intelligence , computer science , mathematics , materials science , combinatorics , composite material
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.

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