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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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使用混合CNN和极端学习机器进行乳腺癌检测和分析.

Vidhushavarshini Sureshkumar1, Rubesh Sharma Navani Prasad2, Sathiyabhama Balasubramaniam3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai 600026, India.

Journal of personalized medicine
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

结合深度学习和极端学习机器的新型混合模型改善了乳腺癌检测. 这种计算机辅助诊断系统增强了对早期诊断和更好的患者结果的细分和分类.

关键词:
乳腺癌 乳腺癌 乳腺癌计算机辅助诊断 (CAD) 是一种计算机辅助诊断.复杂的神经网络是一种复杂的神经网络.极端学习的机器学习.进行乳房造影 (mammogram) 进行乳房造影.乳房肌肉的去除 乳房肌肉的去除

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科学领域:

  • 医疗成像医学成像
  • 医疗保健中的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 早期发现乳腺癌可显著提高全球的生存率.
  • 乳房摄影是一种关键的诊断工具,但图像分析算法仍然存在挑战.
  • 计算机辅助诊断 (CAD) 系统对于提高诊断准确性至关重要.

研究的目的:

  • 开发和评估一种混合计算机辅助诊断 (CAD) 模型,用于增强乳腺癌检测,细分和分类.
  • 用先进的机器学习技术提高乳腺癌诊断的准确性和效率.
  • 为了解决选择适当的算法进行乳房图分析的研究挑战.

主要方法:

  • 开发了一种混合模型,将卷积神经网络 (CNN) 与剪裁组合的极端学习机器 (HCPELM) 结合起来.
  • 该模型使用修正线性单元 (ReLU) 激活功能来进行增强的数据分析和文物删除.
  • 通过结特定层并修改架构来减少有效检测癌症的参数,采用了转移学习技术.

主要成果:

  • 混合HCPELM模型在MIAS数据库上实现了86%的乳腺图像识别准确度.
  • 与基准深度学习模型相比,拟议的模型表现优越.
  • 该系统有效地进行了图像增强,细分,特征提取和分类.

结论:

  • 在早期发现和诊断乳腺癌方面,HCPELM混合分类器表现出卓越的性能.
  • 这种先进的CAD系统可以显著帮助医疗保健从业者诊断乳腺癌.
  • 开发的模型提供了一个有前途的解决方案,以改善乳房影像分析和患者的结果.