通过对大猩猩部队优化和基于MRI图像的旋转学习的综合研究来识别乳腺损伤
Tapas Si1, Dipak Kumar Patra2, Saurav Mallik3
1Department of Computer Science & Engineering, University of Engineering & Management, Jaipur, GURUKUL, Sikar Road (NH-11), Udaipuria Mod, Jaipur, Rajasthan, 303807, India.
Scientific reports
|July 18, 2023
概括
这项研究引入了新的大猩猩部队优化 (GTO) 和改进的GTORBL方法,用于精确的乳腺癌磁共振成像 (MRI) 分段. 这些先进的技术显著提高了早期检测和诊断的性能.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 乳腺癌是全球女性的主要健康问题.
- 乳腺癌病变的早期检测和精确细分对于有效治疗和改善生存率至关重要.
- 磁共振成像 (MRI) 在乳腺癌诊断中起着至关重要的作用,需要先进的细分技术.
研究的目的:
- 研究和开发用于乳腺癌诊断的先进磁共振成像 (MRI) 分段技术.
- 用卡普尔的基于的多层次值来优化DCE-MRI中的乳腺癌病变细分.
- 引入和评估一个改进的大猩猩部队优化 (GTO) 算法,称为GTORBL,以提高细分精度.
主要方法:
- 利用卡普尔的基于的多级值来进行病变细分.
- 应用性大猩猩部队优化 (GTO) 和增强版GTORBL (GTO与旋转对立基于学习),用于优化细分参数.
- 对来自20名患者的100片T2加权斜视图 (T2 WS) DCE-MRI进行了测试,并与各种既有算法进行了比较.
主要成果:
- 基于GTO的方法实现了高的子相似系数 (DSC),灵敏度和准确性.
- GTORBL方法表现出卓越的性能,提高了准确性,灵敏度和DSC值.
- 包括ANOVA和Wilcoxon签名排名测试在内的统计分析证实了拟议方法的显著优异性.
结论:
- 拟议的基于GTO和GTORBL的细分方法与现有的乳腺癌MRI分析技术相比,提供了更高的性能.
- 这些先进的优化算法具有显著的潜力,可以提高乳腺癌诊断的准确性和效率.
- 这些发现表明,开发更有效的AI驱动工具在瘤医学成像中是一个有希望的方向.
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