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一个高效的综合智能系统,用于细分和分类肺癌计算机断层扫描图像

Maheswari Sivakumar¹, Sundar Chinnasamy², Thanabal Ms³

¹Department of AI&DS, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, India.

PeerJ. Computer science

|December 13, 2024

概括

这项研究引入了使用CT图像进行肺癌预测的增强智能系统. 这种新的方法达到99.87%的准确性,改善了肺癌的早期检测和诊断.

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:

肺癌是一个重大的全球健康挑战,死亡率和发病率很高.
从CT图像中精确细分肺癌对于早期检测至关重要,但由于尺寸,形状和周围组织的变化而面临挑战.

研究的目的:

开发和评估一种新的增强组合智能系统,用于从CT图像中准确预测肺癌.
通过提出综合方法来解决自动肺瘤细分方面的困难.

主要方法:

预处理涉及使用平均和自适应中间波器消除噪声,然后进行图形平衡以增强图像.
适应深度信念网络 (ADBN) 与级联受限制的博尔兹曼机器 (RBM) 用于细分受影响的肺部区域和提取特征.
混合螺旋优化-智能通用粗集 (SOI-GRS) 用于特征选择和一个优化的光梯度增强机 (LightGBM) 模型,优化由集团哈里斯优化 (EHHO),用于分类.

主要成果:

拟议的系统在各种数据集上进行了训练和测试,包括LUNA 16,Kaggle数据科学碗,癌症成像档案馆和本地数据集.
使用Python与TensorFlow和Scikit-Learn进行了广泛的实验,证明了系统的有效性.
该模型以最小的分类错误实现了99.87%的分类准确度,准确识别了患有肺癌的个体.

结论:

综合智能系统,结合ADBN和优化LightGBM,显著优于其他预测模型.
这种增强的系统提高了诊断效率,帮助医生和放射科医生更好地诊断肺癌患者.

关键词:

适应的深层信念网络.适应式中间波器适应式中间波器汇集哈里斯的优化哈里斯的优化基因组图均等化基因组图均等化轻 GBM GBM 的时间

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