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通过优化,深度学习和物联网数据传输,通过CT扫描检测肺癌的综合方法

Shaik Karimullah¹, Mudassir Khan², Fahimuddin Shaik¹

¹Department of Electronics and Communications Engineering, Annamacharya Institute of Technology and Sciences (Autonomous), Rajampet, Andhra Pradesh, India.

Frontiers in oncology

|October 22, 2024

概括

这项研究介绍了一种使用图像处理和深度学习的先进肺癌检测工具. 新的CBO+DenseNet CNN模型显著提高了早期肺癌识别的诊断准确性.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

肺癌是一个日益严重的全球健康问题,早期检测对于改善结果至关重要.
目前的查方法在患者选择和风险分层方面面临挑战.
需要先进的诊断工具来提高早期肺癌检测的准确性.

研究的目的:

通过先进的图像处理和深度学习,开发和验证一种用于早期肺癌检测的新型诊断方法.
为了提高肺结节细分和分类的准确性.
提高肺癌诊断系统的整体性能.

主要方法:

在计算机断层扫描 (CT) 图像中使用历史图平衡来去除文物.
实现了以碰撞物体优化 (CBO) 优化的Otsu值,用于精确的肺CT图像细分.
采用局部二进制模式 (LBP) 进行特征提取,包括节点大小和位置.
应用DenseNet深度学习算法来对良性和恶性瘤进行分类.

主要成果:

拟议的CBO+DenseNet CNN模型实现了高性能指标:98.17%的准确性,97.32%的特异性,97.46%的精度,97.89%的回忆.
与传统的诊断方法相比,表现出显著的性能改善.
该模型有效地区分了良性和恶性肺瘤.

结论:

开发的CBO+DenseNet CNN模型显示出作为肺癌先进诊断工具的巨大潜力.
结合优化和深度学习的方法提高了细分和分类的准确性.
这种新的方法在瘤分类和定位方面提供了更高的准确性,用于早期检测肺癌.

关键词:

在美国,CNN是CNN.这就是为什么物联网物联网物联网.一个说话的东西.肺癌是一种肺癌.优化的优化优化优化.

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