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多种分类方法用于肺结节的检测和分类,在X射线图像中提出纹理特征

Mary Jaya Vj¹, Krishnakumar S²

¹Department of Computer Science, Assumption Autonomous College, Changanassery, Kerala India.

Multimedia tools and applications

|June 26, 2023

概括

使用先进的成像和人工智能的早期肺结节检测显著提高了生存率. 这项研究引入了一种新的方法,结合了深度学习和优化的算法,以更准确和高效地识别肺结节.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

肺癌是全球癌症相关死亡的主要原因之一.
通过X射线和CT扫描进行早期检测可以提高存活率,但会增加放射科医生的工作量.
现有的计算机辅助诊断 (CAD) 系统面临着敏感性和错误阳性方面的挑战.

研究的目的:

开发一种先进的自动化系统,用于精确的肺结节 (LN) 检测和分类.
为了提高肺癌诊断的效率和准确性,协助放射科医生.
改善当前CAD系统在灵敏度和假阳性率方面的局限性.

主要方法:

使用历史图平衡 (HE) 进行预处理.
改进了使用层次结构 (BIRCH) 进行细分的平衡代缩减和聚类.
使用灰色级别运行长度矩阵 (GLRM),灰色级别并发矩阵 (GLCM) 和局部向量模式 (LVP) 的特征提取.
使用优化的卷积神经网络 (CNN) 进行分类,并与基于混乱人口的甲虫群算法 (CP-BSA) 进行微调.
使用长短期记忆 (LSTM) 进行结节类型分类 (良性,恶性,正常).

主要成果:

提出的方法实现了高精度 (0.9575) 和灵敏度 (0.9646).
与现有方法相比,在肺结节检测和分类方面表现出卓越的性能.
有效地减少了放射科医生的工作量,同时保持了高的诊断准确性.

结论:

综合人工智能方法为早期和准确的肺癌诊断提供了一个有希望的解决方案.
HE,BIRCH,高级功能,优化的CNN和LSTM的新组合显著提高了LN检测.
该系统有可能通过更早,更精确地检测肺癌来改善患者的治疗结果.

关键词:

在 CP-BSA 算法中.改进的树改善的树肺癌是一种肺癌.优化了CNN的优化方式.拟议的LVP的特点包括:

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