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相关实验视频

Updated: Jul 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

阿尔-比鲁尼地球半径优化与转移学习基于组织病理图像分析,用于肺癌和结肠癌检测.

Rayed AlGhamdi¹, Turky Omar Asar², Fatmah Y Assiri³

¹Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

|July 14, 2023

概括

这项研究引入了一种新的检测肺癌和结肠癌 (LCC) 的方法,该方法使用了他的病理学图像. 阿尔-比鲁尼地球半径优化与转移学习 (BERTL-HIALCCD) 技术提高了早期癌症诊断的准确性.

关键词:

计算机辅助诊断是指计算机辅助的诊断.肺癌和结肠癌是肺癌和结肠癌.医疗图像分析分析参数优化的参数优化转移学习转移学习

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科学领域:

医疗成像医学成像
计算生物学计算生物学
人工智能在医学中的应用

背景情况:

肺癌和结肠癌 (LCC) 的早期诊断对于患者的治疗结果至关重要.
组织病理图像 (HSI) 分析对于LCC诊断至关重要,但耗时且容易出现人为错误.
计算机辅助方法,特别是转移学习 (TL),为自动化HSI分析提供了解决方案.

研究的目的:

开发和评估Al-Biruni地球半径优化与转移学习为基础的肺癌和结肠癌检测组织病理图像分析 (BERTL-HIALCCD) 技术.
为了在基因病理图像中实现LCC的准确和高效检测.

主要方法:

在BERTL-HIALCCD技术集成计算机视觉和转移学习.
一个改进的ShuffleNet模型,通过Al-Biruni地球半径 (BER) 系统优化超参数,用于特征提取.
一个深度卷积循环神经网络 (DCRNN) 用于LCC识别,参数由coati优化算法 (COA) 调整.

主要成果:

在大型HSI数据集上的实验验证证明了BERTL-HIALCCD技术的有效性.
与现有模型相比,Al-Biruni Earth Radius (AER) 和COA算法在癌症检测方面取得了卓越的性能.

结论:

伯特尔-希尔CCD技术提供了一种有效的计算机辅助方法,用于在组织病理图像中检测LCC.
这种方法显示了提高早期癌症诊断的准确性和效率的巨大潜力.