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Updated: Jun 12, 2025

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

Published on: July 25, 2011

提高口腔状细胞癌的检测使用组织病理学图像:一个深度特征融合和改进的哈里斯·霍克斯基于优化的框架.

Amad Zafar¹, Majdi Khalid², Majed Farrash²

¹Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)

|September 27, 2024

概括

这项研究引入了一种人工智能驱动的方法,用于使用基因病理图像进行早期口腔癌检测. 该方法达到97.78%的准确性,为临床诊断提供了一个有前途的工具.

关键词:

机器学习是机器学习.口腔癌是指口腔癌的一种疾病.口腔癌是指口腔癌的发生.口腔状细胞癌的癌症.

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Last Updated: Jun 12, 2025

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

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Published on: July 25, 2011

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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:

口腔癌或口腔状细胞癌 (OSCC) 是一个重要的全球健康问题,早期检测对于改善患者存活率至关重要.
目前的OSCC诊断方法可能耗时,并且可以从自动化的客观分析中获益.
机器学习和图像处理为提高从组织病理图像中检测OSCC的准确性和效率提供了潜力.

研究的目的:

开发和评估一种自动化的机器学习方法,用于检测口腔状细胞癌 (OSCC),使用他的病理图像.
调查深度特征提取,特征融合和优化特征选择的有效性,以提高OSCC分类性能.
评估拟议框架的临床适用性,以帮助医疗专业人员诊断OSCC.

主要方法:

从经过预训练的模型 (ResNet-101,EfficientNet-b0) 中,从基因病理图像中提取了深度特征.
使用正规相关性方法进行特征融合,然后使用二进制改进的哈里斯霍克斯优化 (b-IHHO) 算法进行特征选择.
优化的功能被用来训练一个k-最近邻居 (kNN) 分类器.

主要成果:

拟议的框架实现了97.78%的高OSCC分类率.
b-IHHO算法有效地将特征向量大小减少到平均899个,同时提高了分类性能.
统计分析证实了与其他特征选择方法和最先进的技术相比,b-IHHO方法的稳定性,可靠性和意义 (p < 0.01).

结论:

开发的基于图像处理的机器学习方法证明了OSCC检测的高精度和稳定性.
深度特征提取,融合和b-IHHO优化的结合为提高诊断性能提供了强大的工具.
拟议的框架显示了临床实施的重大潜力,以帮助医疗保健提供者在口腔癌的早期和准确诊断.