自然启发的多层次值与CNN集成,用于在胸部X射线图像中准确地分类COVID-19和肺部疾病
Wafa Gtifa1, Ayoub Mhaouch2, Nasser Alsharif3
1Laboratory of Automation and Electrical Systems and Environment, Monastir National School of Engineers (ENIM), University of Monastir, Monastir 5035, Tunisia.
Diagnostics (Basel, Switzerland)
|June 26, 2025
概括
这项研究引入了一种新的混合框架,用于使用先进的图像细分和卷积神经网络 (CNN) 来从胸部X射线中准确地分类COVID-19. 该方法实现了高精度,为自动化医疗诊断提供了潜在的解决方案.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 诊断技术 诊断技术 诊断技术
背景情况:
- 从胸部X射线进行准确的COVID-19分类是具有挑战性的,因为其与其他肺部疾病的重叠特征.
- 目前用于肺部疾病的诊断方法表现不佳.
- 在肺病理的精确细分和分类方面存在重大诊断差距.
研究的目的:
- 开发一种新的混合框架,用于精确细分和分类肺部疾病,特别是区分COVID-19.
- 解决现有方法在通过胸部X射线准确诊断COVID-19的局限性.
- 提高诊断准确度和识别肺部疾病的效率.
主要方法:
- 一种混合方法,将多级值与元启发式优化算法 (动物迁移优化,电磁性优化,和搜索算法) 结合起来,以增强图像细分.
- 使用卷积神经网络 (CNN) 来对细分的胸部X射线图像进行分类.
- 根据细分的特征将图像分类为COVID-19,病毒性肺炎或正常.
主要成果:
- 拟议的框架实现了特殊的诊断性能:99%的准确性,99%的灵敏性和99.5%的特异性.
- 该方法在临床图像分类任务中证明了其稳定性和有效性.
- 高水平的诊断指标证实了混合方法的可靠性.
结论:
- 这项研究提出了一种新的技术整合解决方案,用于使用胸部X射线进行自动COVID-19诊断.
- 该框架的高精度和计算效率表明,它有可能在现实世界中进行临床部署.
- 这项研究为肺部疾病的医学诊断提供了有前途的进展.
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