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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Updated: Jun 30, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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一个有效的决策支持系统用于白血病的识别利用自然灵感深度功能优化优化.

Muhammad Awais1,2, Md Nazmul Abdal3, Tallha Akram1

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan.

Frontiers in oncology
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概括
此摘要是机器生成的。

这项研究引入了使用先进图像分析诊断急性淋巴细胞白血病 (ALL) 的高效管道. 该方法增强了血液细胞图像和优化特征,在对ALL及其亚型的分类中实现了高精度.

关键词:
在美国,CNN是CNN.生物启发的生物灵感.深度学习是一种深度学习.白血病的分类是白血病的分类.超音响学优化优化方法转移学习转移学习

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科学领域:

  • 医学成像和诊断 医学成像和诊断
  • 计算病理学计算病理学
  • 医疗保健中的人工智能

背景情况:

  • 白血病,特别是急性淋巴细胞白血病 (ALL),由于其具有攻击性,需要快速准确的诊断以获得有效的治疗.
  • 用于白血病诊断的计算机视觉面临来自血液细胞复杂形态和深度神经网络高计算需求的挑战.
  • 现有的方法经常在特征提取和选择方面扎,影响诊断的准确性和效率.

研究的目的:

  • 使用医学图像开发一条有效的计算管道,用于对急性淋巴细胞白血病 (ALL) 的二进制和亚型分类.
  • 通过一种新的邻里像素转换方法,提高血细胞图像的清晰度和可辨别性.
  • 在ALL诊断中提高深度学习模型的特征提取和选择效率.

主要方法:

  • 使用差异进化的一种新的邻近像素转换技术被用来预处理血细胞图像.
  • 一种混合特征提取方法结合了从InceptionV3和DenseNet201深度神经网络模型的转移学习.
  • 定制的二进制灰狼算法用于特征选择,实现了80%的特征大小减少,同时保留关键信息.

主要成果:

  • 该管道实现了98.1%的准确性,98.1%的灵敏度和98%的准确性,用于对公共数据集上的ALL的二进制分类.
  • 对于ALL亚型分类,最佳性能达到98.14%的准确度,78.5%的灵敏度和98%的精度.
  • 拟议的特征选择方法与传统的元启发学相比显示出更高的收性,并实现了与现有技术相比可比或更好的性能.

结论:

  • 开发的高效管道为准确和快速诊断急性淋巴细胞白血病 (ALL) 提供了有希望的方法.
  • 图像转换,混合特征提取和优化特征选择的组合显著提高了分类性能.
  • 这种人工智能驱动的方法有可能改善血液学诊断中的临床决策支持系统.