在肺部CT扫描中改进COVID-19损伤细分,使用群体智能和进化算法
Wafa Gtifa1, Marwa Fradi2, Anis Sakly1
1Laboratory of Automation and Electrical Systems and Environment, Monastir National School of Engineers (ENIM), University of Monastir, Monastir, Tunisia.
概括
群体智能算法在计算机断层扫描 (CT) 扫描COVID-19中有效地细分肺部病变. 粒子集群优化实现了最高的准确性,提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 在计算机断层扫描 (CT) 扫描中准确识别肺部病变对于管理2019年新冠肺炎疾病 (COVID-19) 至关重要.
- 群体智能算法显示了医学成像中自动化病变细分的潜力.
研究的目的:
- 评估和比较四个群体智能算法的性能,以在CT扫描中对COVID-19相关的肺病变进行细分.
- 确定最有效的算法,以提高COVID-19检测中的诊断准确性.
主要方法:
- 该研究实施并比较了四种群体智能算法:引力搜索算法 (GSA),细菌食优化算法 (BFOA),遗传算法 (GA) 和粒子群体优化 (PSO).
- 这些算法被应用到CT图像中的COVID-19的肺部病变细分.
主要成果:
- 基因算法 (GA),引力搜索算法 (GSA) 和细菌食优化算法 (BFOA) 展示了超过90.5%的细分精度.
- 粒子集群优化 (PSO) 实现了最高的细分精度91.45%,F1得分为95.54%.
- 使用优化的群集智能方法,整体细分精度高达99%.
结论:
- 群体和进化算法是细分COVID-19肺病变的有效工具.
- 该研究强调了这些算法的潜力,以提高COVID-19患者的诊断准确性和治疗效率.
关键词:
这就是BFOA.根据COVID-19的情况,在这里,你会看到一条路.在 GSA 中,GSA 是指GSA.公共服务人员 (PSO)计算机断层扫描 (CT) 是一种计算机断层扫描.细分化 细分化的细分化群众情报算法 群众情报算法更多相关视频
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