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基于二进制粒子群优化智能特征优化算法的磁共振成像在肾上腺肿瘤诊断中的应用。

Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor.

机构信息

Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang 222061, Jiangsu, China.

Yingbo Super Computing (Nanjing) Technology Co. Ltd., Nanjing 210000, Jiangsu, China.

出版信息

Contrast Media Mol Imaging. 2022 Feb 28;2022:5143757. doi: 10.1155/2022/5143757. eCollection 2022.

DOI:10.1155/2022/5143757
PMID:35291422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8901308/
Abstract

This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect (  0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) (  0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.

摘要

本研究旨在探讨基于二进制粒子群优化算法（BPSO）的磁共振成像（MRI）在诊断肾上腺肿瘤中的应用价值。选取我院收治的 120 例肾上腺肿瘤患者，随机分为对照组（常规 MRI 检查）和观察组（基于 BPSO 智能特征优化算法的 MRI 检查），每组 60 例。比较两组诊断方法的灵敏度、特异度、准确率和 Kappa 值。结果显示，在相同处理效果下（  0.05），BPSO 算法的计算速率最佳。基于优化算法的 MRI 用于诊断肾上腺肿瘤，观察组的灵敏度、特异度、准确率和 Kappa 值（83.33%、79.17%、81.67%和 0.69）均高于对照组（50%、75%、58.33%和 0.45）。观察组肿瘤位置结果的相似性（89.24%）明显高于对照组（65.9%）（  0.05）。结论：与 SFFS 等算法相比，BPSO 算法在计算速度方面具有更多优势。基于 BPSO 智能特征优化算法的 MRI 对肾上腺肿瘤具有良好的诊断效果和更高的准确率，显示了计算机智能技术在医学领域的良好发展前景。

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