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将殖民地优化与深度学习相结合,改善肺癌诊断和预后

Sujatha Kesavan¹, Malathi Marichamy², Nagarajan Pandian³

¹Department of EEE, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India.

Asian Pacific journal of cancer prevention : APJCP

|January 22, 2026

概括

这项研究通过将殖民地优化 (ACO) 与深度学习模型相结合,提高了肺癌诊断. 整体方法显著提高了准确性,DenseNet在可靠的人工智能驱动的医疗保健解决方案中实现了97.9%.

科学领域:

人工智能在医学中的应用
计算智能是一种计算智能.
医学成像分析医学成像分析

背景情况:

肺癌的诊断依赖于CT图像和医疗记录的准确解释.
目前的诊断方法可以通过先进的计算技术来增强.
人工智能驱动的医疗保健解决方案显示出改善诊断精度的前景.

研究的目的:

提高肺癌诊断的准确性和可靠性.
将殖民地优化 (ACO) 与深度学习模型集成,以提高检测.
建立未来人工智能驱动的瘤医疗保健解决方案的基础.

主要方法:

集体学习将群优化 (ACO) 与深度学习模型相结合:DenseNet,ResNet 50,VGG 19和长短期记忆 (LSTM) 网络.
使用ACO进行特征选择,以优化模型性能.
综合模型被应用于从CT图像和医疗记录诊断肺癌.

主要成果:

整体模型将ACO与深度学习相结合,显著提高了肺癌诊断准确度.
与ACO和LSTM相结合的DenseNet实现了最高的准确率97.9%.
ResNet 50达到96.2%的准确率,VGG 19达到92.3%的准确率,证明了ACO在提高模型性能方面的有效性.

结论:

殖民地优化有效优化特征选择,导致肺癌诊断深度学习模型性能显著改善.
这项研究表明,将群体智能与深度学习相结合,可以实现更精确,更可靠的医学诊断的潜力.
这种方法为人工智能驱动的医疗保健提供了有希望的进步,改善了肺癌诊断和患者的治疗结果.

关键词:

殖民地优化殖民地优化电脑图像扫描 (CT) 扫描深度学习 (Deep Learning) 是一种深度学习.功能选择功能选择肺癌是一种肺癌.

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